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UCGE Reports
Number 20231
Department of Geomatics Engineering
Hardware Simulator Characterization of Assisted GPS
(URL: http://www.geomatics.ucalgary.ca/links/GradTheses.html)
by
Milidu Dharshaka Karunanayake
November 2005
UNIVERSITY OF CALGARY
Hardware Simulator Characterization of Assisted GPS
by
Milidu Dharshaka Karunanayake
A THESIS
SUBMITTED TO THE FACULTY OF GRADUATE STUDIES
IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE
DEGREE OF MASTER OF SCIENCE
DEPARTMENT OF GEOMATICS ENGINEERING
CALGARY, ALBERTA
November, 2005
© Milidu Dharshaka Karunanayake 2005
iii
Abstract The Federal Communications Commission (FCC) E-911 mandate, Location-Based
Services, as well as personal and vehicular navigation applications are driving the need
for navigation capability in degraded signal environments such as in urban areas and
indoors. Since the position accuracy yielded by GPS methods is better than other
positioning technologies, most wireless carriers are looking at Assisted GPS (AGPS) as
the solution to meet the FCC criteria.
In this thesis, the performance of AGPS is analyzed using a hardware simulator
and compared to High Sensitivity GPS (HSGPS) and conventional GPS receivers. It is
found that an AGPS receiver provides greater acquisition sensitivity and similar tracking
performance as an HSGPS receiver. The time-to-first fix (TTFF) is considerably lower
due to the assistance data provided to the AGPS receiver. The effect of aiding
information is also investigated. Results indicate that for weak signals, timing accuracy
has a significant effect on TTFF, with more accurate timing leading to lower TTFFs. In
terms of initial position, as the user to reference distance increases, the TTFF also
increases. No clear trend was observed in the position domain. In terms of Radio
Frequency Interference (RFI), the AGPS receiver was able to tolerate 5 to 10 dB more
interference power than an HS receiver and 10 to 15 dB more than a conventional
receiver in acquisition mode. Both AGPS and HS have similar performance while
tracking. In all RFI cases, all the receivers were able to tolerate more interference while
tracking than in acquisition mode.
iv
Acknowledgements The completion of this thesis would not be possible without the help of many people and
I would like to acknowledge them at this time. I would like to thank …
• My parents, without whom it would not be possible to accomplish what I have so
far in my life. Thank you for being my greatest cheerleaders and when necessary,
for being my critics too. A special thank you to my brother Charaka.
• My supervisor, Dr. Elizabeth Cannon; she gave me an opportunity to apply my
knowledge in Electrical Engineering in a different field and consequently,
renewed my enthusiasm, my lifelong thirst to learn. Thank you for your financial
support, encouragement, patience and mentoring and for allowing me to be
myself throughout my studies.
• My co-supervisor Dr. Gérard Lachapelle. Thank you for your financial assistance,
positive feedback and your great sense humour, which provided many laughs.
After all, laughter is the best medicine.
• SiRF Technology Inc. for proving us with the AGPS receiver. A special thank
you to Geoff Cox for answering my many questions. Lionel Garin and Greg
Turetzky at SiRF are also acknowledged.
• Sanjeet Singh for his friendship; we have travelled the same road (at least in terms
of academics) since 1997 and it has been quite an adventure.
• Tao Hu for being the best officemate that a guy like me, who loves to talk, can
ask for. Xie xie!
• Sameet Deshpande for your friendship and expertise.
• My colleagues in Geomatics Engineering:
Haitao Zhang and Ping Lian for exposing me to the Chinese culture
PLAN group members that have worked with me since the start of my
studies especially Salman Syed, Rob Watson, Mark Petovello, Olivier
Julien, Nyunook Kim, Scott Crawford, Diep Dao, Anastasia Salycheva,
v
Chaminda Basnayake, Bo Zheng, Seema Phalke and Glenn MacGougan.
Thank you all for making the PLAN group a fun place to work.
Victoria Hoyle, Natalya Nicholson and Ruben Yousef
• God for all that he has given me. “Every good and perfect gift is from above”
James 1:17.
vi
Dedication To my parents, for all their support, guidance and love
Across 2. Frequency shifts 4. Data 7. AGPS receiver 8. LBS device 12. Thesis topic 16. MS 18. Satellite orbit
Down 1. Author 3. Satellite 5. Cell 6. First fix time 8. Almighty 9. User 10. Direct signal 11. Positioning system 13. Unaided 14. Technology to transmit codes 15. Interference 17. Reference receiver
Answers: Go to http://www.geocities.com/dhardevil/puzzle
vii
Table of Contents Abstract ............................................................................................................ iii Acknowledgements........................................................................................... iv
Dedication .......................................................................................................... vi Table of Contents ............................................................................................. vii List of Tables ...................................................................................................... x
List of Figures................................................................................................... xii List of Abbreviations........................................................................................ xv
CHAPTER 1: INTRODUCTION..................................................................... 1
1.1 Relevant Research............................................................................................... 4
1.2 Research Objectives............................................................................................ 5
1.3 Thesis Outline ..................................................................................................... 8
CHAPTER 2: GPS, HSGPS and AGPS .............................................................. 9
2.1 GPS Overview .................................................................................................... 9
2.2 GPS Signal Structure ........................................................................................ 10
2.2.1 Spread Spectrum Basics...................................................................... 11
2.2.2 Code Division Multiple Access ........................................................... 13
2.2.3 L1 and L2 Signals ............................................................................... 13
2.2.4 Auto Correlation ................................................................................. 15
2.2.5 Cross Correlation ............................................................................... 17
2.3 GPS Observations and Error Sources ............................................................... 18
2.3.1 Pseudorange ....................................................................................... 18
2.3.2 Carrier Phase...................................................................................... 20
2.3.3 Doppler Measurement ........................................................................ 21
2.3.4 Error Sources...................................................................................... 22
2.4 Receiver Architecture ....................................................................................... 26
2.5 High Sensitivity GPS ........................................................................................ 28
viii
2.6 Assisted GPS..................................................................................................... 33
2.6.1 Aiding Parameters .............................................................................. 36
2.6.2 Timing Information ............................................................................. 38
2.6.3 AGPS Field Tests ................................................................................ 41
CHAPTER 3: Acquisition and Tracking Tests ..................................... 43
3.1 Hardware GPS RF Simulation .......................................................................... 43
3.2 Test Setup.......................................................................................................... 45
3.3 Acquisition........................................................................................................ 49
3.3.1 All Satellites with the Same Power ..................................................... 49
3.3.2 One Satellite with a Strong Signal ...................................................... 53
3.4 Tracking ............................................................................................................ 56
3.5 Chapter Summary ............................................................................................. 62
CHAPTER 4: Assistance Data ......................................................................... 63
4.1 Timing Accuracy .............................................................................................. 63
4.2 Initial Position................................................................................................... 70
4.3 Position Uncertainty.......................................................................................... 77
4.4 Chapter Summary ............................................................................................. 82
CHAPTER 5: RF Interference on AGPS................................................. 84
5.1 Sources of RF Interference ............................................................................... 84
5.2 Interference Effects........................................................................................... 89
5.3 Interference Tests.............................................................................................. 92
5.3.1 CW Interference .................................................................................. 93
5.3.2 AM Interference .................................................................................. 98
5.3.3 FM Interference ................................................................................ 102
5.4 Chapter Summary ........................................................................................... 107
ix
CHAPTER 6: User Dynamics on AGPS................................................. 108
6.1 Effect of Velocity............................................................................................ 108
6.1.1 Acquisition ........................................................................................ 108
6.1.2 Tracking ............................................................................................ 114
6.2 Effect of Acceleration ..................................................................................... 116
6.2.1 Acquisition ........................................................................................ 116
6.2.2 Tracking ............................................................................................ 119
6.3 Chapter Summary ........................................................................................... 121
CHAPTER 7: Conclusions and Recommendations .......................... 123
7.1 Conclusions..................................................................................................... 123
7.2 Recommendations for Future Work................................................................ 125
REFERENCES................................................................................................ 128
Appendix A: Network-based Positioning Technologies .............. 136
x
List of Tables
Table 2.1: Cross correlation probability of C/A code [from Kaplan, 1996]..................... 18
Table 2.2: GPS Error Sources [from Lachapelle, 2002]................................................... 24
Table 2.3: Processing Gain Example ................................................................................ 30
Table 3.1: Acquisition Performance (All Same Power) ................................................... 50
Table 3.2: Acquisition Performance (One Strong Signal) ................................................ 53
Table 3.3: Tracking Performance ..................................................................................... 56
Table 3.4: Average Number of Satellites Tracked............................................................ 59
Table 3.5: 2D and 3D RMS Errors ................................................................................... 60
Table 4.1: 2D and 3D RMS Errors for Different Timing Accuracies .............................. 68
Table 4.2: Average Number of Satellites for Different Timing Accuracies..................... 68
Table 4.3: 2D RMS Errors for Different Initial Position Offsets ..................................... 74
Table 4.4: 2D RMS Errors for Different User to Reference Distances with a Fixed
Horizontal Uncertainty.................................................................................... 77
Table 4.5: 2D RMS Errors for Different Horizontal Uncertainties (11 km User-to-
Reference Distance) ........................................................................................ 81
Table 4.6: 2D RMS Errors for Different Horizontal Uncertainties (30 km User-to-
Reference Distance) ........................................................................................ 82
Table 5.1: Types of RFI and possible sources [Kaplan, 1996]......................................... 85
Table 5.2: Mobile Frequencies and Power Levels [from Paddan et al., 2003]................. 86
Table 5.3: Acquisition and Tracking Threshold under CW Interference ......................... 94
Table 5.4: Acquisition and Tracking Threshold under AM Interference ......................... 98
xi
Table 5.5: Acquisition and Tracking Threshold under FM Interference ........................ 103
Table 6.1: 2D RMS Errors for Different Accelerations (Acquisition Test) ................... 119
Table 6.2: 2D RMS Errors for Different Accelerations (Tracking Test)........................ 121
xii
List of Figures
Figure 1.1: Gizmondo GPS Mobile Device [www.gizmondo.com]................................... 3
Figure 2.1: GPS Signal Spectrum [from Deshpande, 2004]............................................. 11
Figure 2.2: GPS Satellite Transmitter Unit [from Spilker and Parkinson, 1996] ............ 15
Figure 2.3: GPS Receiver Architecture ............................................................................ 26
Figure 2.4: AGPS System................................................................................................. 34
Figure 3.1: Spirent GSS6560 Hardware Simulator........................................................... 44
Figure 3.2: Schematic of Simulator Test Setup ................................................................ 46
Figure 3.3: Test Setup Components.................................................................................. 47
Figure 3.4: TTFF for AGPS for 125 µs Timing (All Satellites Same Power) .................. 52
Figure 3.5: TTFF for AGPS 125 µs Timing Accuracy (One Strong Signal).................... 55
Figure 3.6: Average C/N0 (for all satellites) during Tracking Test .................................. 58
Figure 3.7: Number of Satellites Tracked during Tracking Test ...................................... 59
Figure 3.8: Tracking Test – Position Errors...................................................................... 60
Figure 4.1: Test Setup for Timing and Assistance Data ................................................... 64
Figure 4.2: Normalized TTFF for Different Timing Accuracies...................................... 65
Figure 4.3: Normalized TTFF for 125 µs Timing Accuracy ............................................ 65
Figure 4.4: Position Errors for Different Timing Accuracies at -138 dBm...................... 67
Figure 4.5: Position Errors for Different Timing Accuracies at -140 dBm...................... 67
Figure 4.6: Scenario Representation for a User-to-Reference Distance of 22 km............ 72
Figure 4.7: Average TTFF for Different User-to-Reference Distances............................ 73
Figure 4.8: Position Errors for 2 km and 55 km Initial Position Offsets .......................... 73
xiii
Figure 4.9: TTFF Comparison of Initial Position Offsets for Two Scenarios .................. 76
Figure 4.10: Scenario Representation (11 km User-to-Reference Distance).................... 79
Figure 4.11: TTFF Performance for Different Horizontal Uncertainties ......................... 80
Figure 4.12: Position Errors for 5 km and 50 km Horizontal Uncertainties (11 km User-
to-Reference Distance)................................................................................. 81
Figure 5.1: Interference Test Setup................................................................................... 93
Figure 5.2: 2D Error vs. Relative CW Interference Power (Acquisition) ........................ 96
Figure 5.3: 2D Error vs. Relative CW Interference Power (Tracking)............................. 96
Figure 5.4: 2D RMS Error for each CW Interference Power Interval.............................. 97
Figure 5.5: 2D Error vs. Relative AM Interference Power (Acquisition) ...................... 100
Figure 5.6: 2D Error vs. Relative AM Interference Power (Tracking)........................... 100
Figure 5.7: 2D RMS Error for each AM Interference Power Interval............................ 101
Figure 5.8: Average C/N0 vs. the Relative FM Interference Power (Acquisition) ......... 104
Figure 5.9: Average C/N0 vs. the Relative FM Interference Power (Tracking) ............. 104
Figure 5.10: 2D Error vs. Relative FM Interference Power (Acquisition) ..................... 105
Figure 5.11: 2D Error vs. Relative FM Interference Power (Tracking) ......................... 105
Figure 5.12: 2D RMS Error for each FM Interference Power Interval .......................... 106
Figure 6.1: Dynamics Test Setup.................................................................................... 109
Figure 6.2: Dynamics Test Trajectory ............................................................................ 110
Figure 6.3: Average TTFFs for Different Velocities ...................................................... 111
Figure 6.4: 2D Position Error for AGPS (Acquisition) .................................................. 112
Figure 6.5: 2D Position Error for HSGPS (Acquisition) ................................................ 112
xiv
Figure 6.6: AGPS Position Errors for 72 km/h, 180 km/h and 360 km/h....................... 113
Figure 6.7: AGPS Velocity Errors for Acquisition Test................................................. 114
Figure 6.8: 2D Position Error for AGPS and HSGPS (Tracking) .................................. 115
Figure 6.9: AGPS Velocity Errors for Tracking Test ..................................................... 116
Figure 6.10: Average TTFFs for Different Accelerations .............................................. 117
Figure 6.11: AGPS Position Errors for 0.5 m/s2 and 4 m/s2 (Acquisition) ..................... 118
Figure 6.12: AGPS Position Errors for 0.5 m/s2 and 4 m/s2 (Tracking) ......................... 120
xv
List of Abbreviations
Abbreviations and Acronyms
ADC Analog-to-Digital Converter AGC Automatic Gain Controller AGPS Assisted GPS A-GNSS Assisted GNSS ALI Automatic Location Identification AM Amplitude Modulation AOA Angle of Arrival BOC Binary Offset Carrier BPSK Binary Phase Shift Keying C/A Coarse-Acquisition C/N0 Carrier-to-Noise CDGPS Canada-wide Differential GPS CDMA Code Division Multiple Access CW Continuous Wave DGPS Differential GPS DLL Delay Lock Loop DOP Dilution of Precision DS Direct Sequence DSP Digital Signal Processor E-911 Enhanced 911 EOTD Enhanced Observed Time Difference FAA Federal Aviation Administration FCC Federal Communications Commission FM Frequency Modulation GNSS Global Navigation Satellite System GPS Global Positioning System GSM Global System for Mobile Communications HDOP Horizontal Dilution of Precision HSGPS High Sensitivity GPS IF Intermediate Frequency LBS Location-Based Services LMU Location Measurement Unit LNA Low Noise Amplifier
xvi
LOS Line-of-Sight MS Mobile Station NDU Navigation Data Unit P-code Precise Code PLL Phase Lock Loop PN Pseudo Noise PPS Precise Positioning Service PRN Pseudo Random Noise PVT Position, Velocity & Time RFI Radio Frequency Interference RMS Root Mean Square SNR Signal-to-Noise Ratio SPS Standard Positioning Service SV Satellite Vehicle TCXO Temperature Compensated Crystal Oscillator TOA Time of Arrival TTB Time Transfer Board TTFF Time-To-First Fix UERE User equivalent Range Error UHF Ultra High Frequency VDOP Vertical Dilution of Precision VHF Very High Frequency WAAS Wide Area Augmentation System WLAN Wireless Location Area Network
1
CHAPTER 1: INTRODUCTION
The Global Positioning System (GPS) has become a critical part of the navigation
infrastructure not only within the United States but also in other nations around the
world. With the introduction of the Enhanced-911 (E-911) mandate from the Federal
Communications Commission (FCC) in the United States, it has now become necessary
to provide positions in various environments including urban canyons and indoors. These
environments are referred to as weak/degraded signal environments since the GPS line-
of-sight (LOS) signal can be attenuated by as much as 20-25 dB or more [MacGougan,
2003]. This mandate requires all cell phone service providers to be able to determine the
positions of users by December 31, 2005. The mandate specifies that a mobile 911 caller
location must be established for 67 percent of calls to within 50 metres, and 95 percent of
calls to within 150 metres for wireless handset-based solutions. The requirements are less
stringent for network-based positioning, with 67 percent of calls to within 100 metres,
and 95 percent of calls to within 300 metres [FCC, 2003]. The FCC equivalent body in
Canada, the Canadian Radio-television and Telecommunications Commission (CTRC)
has not yet mandated E-911 Phase II requirements; hence the Canadian carriers are not
pressured to rollout automatic location identification (ALI) technology across their
networks [Mindbranch, 2005].
Wireless carriers have two basic options for meeting the FCC mandate: handset-
based solutions such as Assisted GPS (AGPS) or network-based positioning
technologies. In AGPS, “aiding” parameters are provided by the network to a GPS
2
receiver in a cellular handset to assist in GPS signal acquisition. Aiding improves the
sensitivity of the GPS receiver, allowing it to operate in weak signal environments such
as urban canyons and indoors since conventional GPS receivers (without aiding or high
sensitivity methods) are intended to be used in open sky areas. With network-based
positioning technologies such as Time of Arrival/Time Difference of Arrival
(TOA/TDOA) and Angle of Arrival (AOA), the mobile network in conjunction with
network-based position determination equipment is used to position the mobile device
[Klukas, 1997]. These technologies, which are discussed in Appendix A, require wireless
telecom carriers to make modifications to existing cell sites and the position accuracy
yielded from these techniques is generally lower than GPS-based solutions [LaMance et
al., 2002].
The convergence of wireless technology and GPS has led to the development of a
new set of applications to serve the location-based needs of users. These applications are
known as Location-Based Services (LBS). The intent of LBS is to use accurate real-time
user position information to connect users to nearby points of interest (such as retail
businesses, public facilities or travel destinations), to advise them of current conditions
(such as traffic and weather), or to provide routing and tracking services [Liu, 2000].
The impact of GPS has even entered mobile gaming. GPS-enabled video games
played on mobile devices such as the Gizmondo and location-enabled mobile phones are
adding another dimension to gaming. Gizmondo, which is shown in Figure 1.1, integrates
a GPS receiver (SiRFstarIIe/LP 12-channel GPS chipset), an antenna and SiRFXTrac
high sensitivity tracking software to deliver surreal gaming experiences, and a host of
3
LBS such as “Where Am I,” “Find the Nearest,” and “Tracking.” [Whitford, 2005;
Gizmondo, 2005]
Figure 1.1: Gizmondo GPS Mobile Device [www.gizmondo.com]
For LBS, instead of using GPS, other positioning technologies can also be used
but most LBS applications require outdoor position accuracies of 50 metres or better.
Typical GPS positioning accuracy is 5-10 metres outdoors which is considerably better
than positioning accuracies provided by other positioning technologies. The E-911
mandate, LBS, as well as personal and vehicular navigation applications, are driving the
need for navigation capability in degraded signal environments such as in urban areas and
indoors. Since the position accuracy yielded by GPS methods is better than other
positioning technologies, most wireless carriers are looking at AGPS as the solution to
meet the FCC criteria.
4
1.1 Relevant Research
The GPS L1 carrier is modulated with the Coarse-Acquisition (C/A) code for civilian use.
The signal containing this code, which repeats every millisecond, can be integrated for
extended periods in order to obtain a higher signal-to-noise ratio (SNR) [Peterson et al.,
1995]. Conventional GPS receivers typically use integration times which are less than the
nominal maximum 20 ms coherent interval and are limited in terms of their operational
environments to where there are strong signals (-130 dBm). In order to extend the
capabilities of GPS into many indoor environments where a receiver has to acquire
signals with power levels of -150 dBm or lower, High Sensitivity GPS (HSGPS)
receivers were designed. These receivers employ longer dwell times which can be used to
acquire GPS signals at very low power levels [Chansarkar and Garin, 2000]. In general,
the coherent integration period is limited to 20 ms due to the navigation bits as well as
residual frequency errors during the coherent integration period. Residual frequency
errors are caused by satellite motion, receiver clock instability and user motion-induced
Doppler effects [ibid]. External timing information can be used to extend coherent
integration for more than 20 ms [Krasner et al., 2002; Shewfelt et al., 2001].
The ability to acquire and track weak GPS signals depends on the capabilities of
the receiver to maximize the coherent integration interval prior to non-coherent
accumulation while minimizing residual frequency errors during coherent integration.
The non-coherent integration period, which is the squared output of the coherent interval,
can be much longer compared to coherent integration. However, this procedure results in
a squaring loss [Ray, 2003].
5
There are a number of factors affecting HSGPS performance that have to be taken
into consideration in the design process of the receivers. The thermal noise should be
minimized to maintain tracking and avoid carrier tracking error. In addition, residual
frequency errors should be reduced, which can be accomplished by using a more stable
oscillator [MacGougan, 2003].
The total dwell-time of HSGPS receivers can be up to hundreds of milliseconds
while for conventional GPS it is less than the 20 ms coherent integration interval
maximum. In general, high sensitivity methods can be implemented in either aided or
unaided modes. In unaided mode, the high sensitivity receiver lacks the ability of the
aided receiver to acquire weak signals if it has no a priori knowledge about GPS time and
the current position. However, if an HSGPS receiver is initialized with assistance data by
first acquiring and tracking four or more GPS satellites with strong signals, it has the
same functional capability as an AGPS receiver as long as it can maintain timing,
approximate position, and satellite ephemeris. In aided mode (AGPS), initialization in a
strong signal environment is unnecessary as the network provides the assistance data.
1.2 Research Objectives
When performing GPS testing, two options are available: field testing and simulator
testing. The advantage of field testing is that the results obtained indicate the performance
in real-world conditions. However, the disadvantages are that field testing is more time
consuming and often more expensive than simulator testing. Furthermore, repeatability is
almost impossible in the field. In recent years, advances in simulation technology have
6
contributed to the development of state-of-the-art hardware GPS RF signal simulators.
The simulators provide a controllable environment and results can be verified with
multiple tests that assess repeatability. In this thesis, the latter option is employed. All of
the testing was conducted using the Spirent GSS6560 hardware simulator.
Over the years, extensive testing has been conducted in HSGPS. High-sensitivity
receiver development since 2000 has yielded receivers capable of tracking signal levels
well below nominal levels in real-time [Chansarkar and Garin, 2000]. MacGougan et al.
[2002] demonstrated the operation of the SiRFstarII HSGPS receiver indoors, most
notably demonstrating an ability to track multiple satellites to levels at least 9 to 10 dB
below that which standard receivers can track, and 25 dB below nominal levels. In a
garage environment, the availability of a solution with the SiRF receiver was greater than
99%, with horizontal position-domain errors near 10 m RMS observed. Further similar
tests by Lachapelle et al. [2004] in a North American residence show similar solution
availability of greater than 98% with position errors of approximately 17 m RMS
horizontally. While results as discussed above are promising, these tests focused on
tracking in an indoor environment. In all of the tests, outdoor acquisition was required in
order to initialize the receivers before indoor operation. Thus, in recent years, more focus
has been on testing GPS augmentations such as AGPS and hybrid systems that provide
better performance.
In terms of AGPS testing, field tests have been conducted by SiRF Technology
Inc. [Garin et al., 1999; Garin et al, 2002], Global Locate Inc. [van Diggelen, 2001b] and
QUALCOMM [Biacs et al, 2002]. Syrjärinne [2001] discusses AGPS in detail but from a
7
theoretical perspective and his major contributions are mostly related to GPS time
recovery and decision-based integrity monitoring. However, little research has been
conducted to investigate the effect of aiding parameters on AGPS acquisition and
tracking performance which is the major thrust of this research. The objectives of this
thesis are as follows:
• Assess the fundamental signal acquisition and tracking capability of an AGPS
receiver under weak signal conditions
• Investigate the effect of timing accuracy and approximate position as well as
other aiding parameters on AGPS acquisition and tracking performance
• Investigate the acquisition and tracking performance of an AGPS receiver in
the presence of Radio Frequency interference (RFI), and
• Investigate the effects of user dynamics on AGPS acquisition and tracking
performance
SiRF Technologies Inc. has developed an AGPS receiver (SiRFLocTM) capable of
making measurements in GPS signal degraded environments. All of the testing done in
this thesis will use this receiver. The SiRFLoc receiver will be discussed more thoroughly
in Chapter 3.
Results from simulation tests conducted using a hardware simulator will be
quantified in terms of Time-To-First-Fix (TTFF) as well as position accuracy.
8
1.3 Thesis Outline
This thesis is organized in the following manner: Chapter 1 has introduced the need for
AGPS as well as the research objectives. Chapter 2 gives an overview of GPS, HSGPS
and AGPS. The GPS receiver architecture is also presented in this chapter. In Chapter 3,
the acquisition and tracking simulation tests performed to obtain the sensitivity of an
AGPS receiver are presented.
In Chapter 4, the effects of various aiding parameters on AGPS acquisition are
investigated. Parameters such as timing uncertainty, initial user position and position
uncertainty are examined. Chapter 5 investigates the effect of various RFI sources on
AGPS acquisition and tracking. In this chapter, Continuous Wave (CW), Amplitude
Modulation (AM) and Frequency Modulation (FM) in-band interference will be studied.
AGPS performance under user dynamics is presented in Chapter 6. Finally, in
Chapter 7, conclusions obtained from research as well as recommendations for future
work are provided.
9
CHAPTER 2: GPS, HSGPS and AGPS
This chapter gives a brief overview of the GPS system including the signal structure and
various measurements. It also discusses the theory behind High Sensitivity GPS and
Assisted GPS.
2.1 GPS Overview
The GPS is a satellite-based radio navigation system capable of providing position in
most places and environments in the world. This system was developed by the US
Department of Defense to support military forces by providing world-wide, real-time
position and timing information [Parkinson et al., 1995]. Even though it was originally
developed for the military, GPS is widely used in civilian applications [Spilker and
Parkinson, 1996]. Presently, the system currently consists of 27 (nominally 24) satellites
which provide continuous information for the user to compute three dimensional position
and velocity as well as time (PVT). The satellites orbit approximately 20,000 km above
the Earth’s surface (26,000 km from the Earth’s centre) and have an orbital period of
11 hours 58 minutes [ICD, 2003]. The principle behind GPS is one-way TOA ranging
whereby the user determines the TOA of the signals transmitted by the GPS satellites.
These ranges are used to compute the user navigation solution. A 3D position
computation requires range information from at least three satellites. However, since the
user GPS receiver clock is not synchronized with the satellite clocks, an additional
satellite measurement is required to solve for the receiver clock offset [Kaplan, 1996].
10
GPS operates on two signal frequencies, L1 (1575.42 MHz) and L2
(1227.60 MHz), using code division multiple access (CDMA) technology to transmit the
ranging codes [ICD, 2003]. GPS provides different accuracy levels for civilian and
military users. Civilian users have access to the C/A-code which provides the Standard
Positioning Service (SPS) while military users use a Precise (P)-code to get the Precise
Positioning Service (PPS). The P-code is encrypted and hence not available for civilian
users. The SPS provides an accuracy of 36 m (2D RMS 95%) in the horizontal plane and
77 m (95%) in the vertical direction [Stenbit, 2001]. However, recent field tests have
shown 2D accuracies of 2 m (1-σ RMS) with dual frequency single point positioning
[e.g. Cannon et. al, 2004].
2.2 GPS Signal Structure
The current GPS signal structure was specifically developed for positioning military
personnel and hence required good resistance to jamming signals [Parkinson et al., 1995].
The spread spectrum concept was used to transmit ranging codes to provide the desired
anti-jamming performance. A pseudo random noise (PRN) sequence with a high chipping
rate was used to transmit the navigation information onto the GPS frequencies [ICD,
2003]. Spread spectrum signals have power levels below the noise level and can be
recovered only with an appropriate spreading code. The two spreading codes used in the
current GPS signals are the C/A-code and the P-code. These spreading codes were
selected from a family of Gold codes [Kaplan, 1996]. As previously mentioned, each
satellite transmits the signal on two frequencies (L1 and L2) with the P-code present on
11
both the frequencies. The C/A-code is transmitted only on L1. The CDMA technique of
transmitting different spreading codes for each satellite on the same frequency is used to
distinguish the signals from the different satellites [ICD, 2003]. The current GPS signal
structure is shown in Figure 2.1. The concept of spread spectrum and CDMA are
discussed briefly in the next two sections.
Figure 2.1: GPS Signal Spectrum [from Deshpande, 2004]
2.2.1 Spread Spectrum Basics
The spread spectrum concept consists of transmitting information over a large bandwidth
and using a PRN sequence to spread the information [Peterson et al., 1995]. The amount
of bandwidth required for transmission is determined by the PRN sequence bandwidth. It
should be noted that all modulation techniques which use a bandwidth wider than
required for transmission are not spread spectrum techniques. The spread spectrum
technique is useful for long distance communication with less interference problems
[Kaplan, 1996]. During the recovery of the spread spectrum signal, any interference
signal is spread thereby reducing its power level below the noise. Spread spectrum solves
two important communication problems, namely pulse jamming and low probability of
12
detection [Peterson et al., 1995]. The pulse jammer power level is reduced during signal
recovery in the spread spectrum method. The spread spectrum can be recovered only
when the PRN signal used for spreading is known [ibid]. This reduces the chance of
signal detection by other users in the same frequency band.
For GPS signals, direct sequence (DS) spread spectrum is used. It consists of
modulating the information signal using a spreading carrier signal [ibid]. A binary phase
shift keying (BPSK) signal is used to spread (modulate) the navigation data signal. The
BPSK signal is a square wave (±1) and the phase of the modulated signal changes by 180
degrees with a change in the sign of the signal. Consider a data modulated carrier signal,
S(t):
))(cos()( ttAtS Φ+= ω (2.1)
where
A is the amplitude of the carrier signal (Volts),
ω is the carrier frequency (Hz), and
Φ is the data modulation signal.
BPSK spreading is performed by multiplying the S(t) by a function c(t), which
represents the spreading waveform and the resulting signal, ST(t):
))(cos()()( tttActTS Φ+= ω (2.2)
This spread spectrum signal is then transmitted and is received by the receiver
after a delay of T. To recover the signal, the receiver must replicate the spreading signal
13
used at the transmitter and match the phase of the spreading signal. The received signal,
SR(t), is given as follows:
))(cos()()( φω +−Φ+−= TttTtActRS (2.3)
where φ is the random phase error (radians).
The spreading signal, c(t), has values of ±1, which when multiplied with the
received signal c(t-T) will have a value of one when the phase of the replica signal
matches the incoming signal. This allows for the recovery of the information in Equation
2.3 except for some random phase error [Tsui, 2000]. A concept similar to the one
described above is used in GPS for transmission and recovery of information.
2.2.2 Code Division Multiple Access
A CDMA signal is a spread spectrum signal with all the signals using the same centre
frequency. The spreading codes used are a set of orthogonal or near-orthogonal codes
[Kaplan, 1996]. An orthogonal code has zero correlation with the other codes used in the
system. The codes do not have zero cross-correlation due to side lobes of the codes and
hence there is a possibility of a cross-correlation peak, resulting from correlation between
the same or different codes, being higher than the autocorrelation peak when the desired
signal is weak. Auto-correlation and cross-correlation will be discussed in Sections 2.2.4
and 2.2.5.
2.2.3 L1 and L2 Signals
As previously stated, GPS satellites transmit on two frequencies on the L-band frequency
called L1 and L2. The two carrier frequencies are modulated by the spread spectrum
codes with a unique PRN associated with each satellite. The signals are further modulated
14
by a 50 Hz navigation data message [ICD, 2003]. The C/A and P-codes are in quadrant
phase with each other on the L1 frequency. The C/A-code is 1023 bits long and is
available to civilian users. The P-code is a complex binary sequence, approximately
266.4 days long and is allocated such that each satellite transmits a one week portion of
the entire sequence. Since the P-code is reserved for military applications, it is encrypted
using a Y-code. This encrypted code is transmitted instead of the P-code on both
frequencies [ibid].
A GPS satellite uses a 10.23 MHz reference clock to generate both the L1 and L2
frequencies. The reference clock is usually a cesium standard and generates a clock
frequency slightly lower than 10.23 MHz to account for relativistic effects [Spilker and
Parkinson, 1996]. The GPS signal broadcast on the L1 and L2 frequencies have the
following signal structure [Kaplan, 1996]:
)t1f2sin()t(N)t(C/A1A)t1f2cos()t(N)t(P1A)t(1L π+π= (2.4)
)t2f2cos()t(N)t(P2A)t(2L π= (2.5)
where
A1 is the L1 signal amplitude,
A2 is the L2 signal amplitude,
P(t) is the P-code,
C/A(t) is the C/A-code,
N(t) is the navigation data,
cos(2π f1t), sin(2π f1t), cos(2π f2t) are the unmodulated L1 and L2 signals,
and
15
L1(t) and L2(t) are the modulated L1 and L2 signals.
Figure 2.2 shows a block diagram of the GPS satellite transmitter unit. The
navigation data unit (NDU) generates the cosine and sine of the carrier signal which are
modulated by a 50 Hz navigation data signal. This modulated signal is then spread using
the C/A-code and the P(Y)-code [Kaplan, 1996]. The NDU block performs the function
of modulating the signal, and the synthesizer is used to manipulate the signals according
to the bandwidth specifications of the signal. For the L1 signal, the combiner combines
the C/A-code and the P(Y)-code signals onto one signal. Both the L1 and the L2 signals
are transmitted to the Earth using an L-band antenna.
Figure 2.2: GPS Satellite Transmitter Unit [from Spilker and Parkinson, 1996]
2.2.4 Auto Correlation
The autocorrelation characteristics of GPS PRN codes are fundamental to the signal
acquisition and demodulation processes in a GPS receiver [Tsui, 2000]. The correlation
of a code with itself is called autocorrelation, while the correlation between two codes is
called cross-correlation, which will be discussed in the next section. The autocorrelation
function involves replicating the code and then shifting its phase while multiplying it
with the original function. When the phases of the two signals match, the maximum
correlation is obtained. The autocorrelation function for a Pseudo Noise (PN) sequence,
Navigation Data Unit
(NDU)
L-band synthesizer
Combiner L-band
antenna
16
PN(t), whose amplitude is ±A, chipping period is Tc and period is NTc is given by
Equation 2.6 [Macabiau et al., 2001].
∫ +=cT
c
dttPNtPNT
R0
)()(1)( ττ (2.6)
A PN sequence of length N = 2n-1, where n is the number of shift register stages
used to generate the sequence is called a maximum length sequence [Kaplan, 1996]. The
autocorrelation function yields –A2/N outside the correlation interval because the number
of negative values (-1) is always one greater than number of positive values (+1) in a
maximum length PN sequence [Peterson et al., 1995]. An autocorrelation function for a
maximum length PN sequence is the infinite series of triangular functions with period
NTc. The negative correlation amplitude (–A2/N) is obtained when the phase shift,τ, is
greater than ±Tc, (or multiples of ±Tc(N±1)) and represents a constant term in the series
[Macabiau et al., 2001].
GPS PRN codes have periodic correlation triangles and a peak spectrum that has
similar characteristics to the maximum length PN sequences [Kaplan, 1996]. However
the GPS codes are not maximum length PN sequences. A simple 10-bit linear code
generator can generate 1023 sequences but all the autocorrelation functions have
considerable power in the side lobes which affects the signal detection at low signal
strengths. This problem was overcome by combining sequences from two 10-bit shift
registers (G1 and G2) to generate the C/A-code [Spilker and Parkinson, 1996]. The
combination of two sequences from the C/A-code generator yields 1023 possible
combinations. The correlation properties of these sequences were studied and 32 codes
17
with the best cross-correlation properties were selected for the GPS satellites [Kaplan,
1996].
The autocorrelation function of the GPS C/A-code has the same period and shape
in the correlation domain as the maximum length PN sequences. However, there are
small correlation values in the interval between the maximum correlation intervals. These
small fluctuations in the autocorrelation function of the C/A-code result in the deviation
of the line spectrum from the sin(x)/x envelope [Spilker and Parkinson, 1996]. The
1 KHz line spectrum spacing is the same for all the C/A-codes and the 10-bit maximum
length sequence code. The ratio of power in each of the C/A-code line spectrum to the
total power can fluctuate by nearly 8 dB with respect to the -30 dB levels that would be
obtained if every line contained the same power [Kaplan, 1996]. The autocorrelation
function of the P-code has similar characteristics to the C/A-code.
2.2.5 Cross Correlation
A GPS receiver generates a replica of the GPS PRN code and shifts its phase to align
with the PRN code for each satellite. The PRN codes for different satellites should have
poor cross-correlation properties among them to allow acquisition of the correct PRN
signal. The GPS C/A-code length is 1023 chips which causes the cross-correlation
properties to be poor for some codes. The C/A-code autocorrelation peaks are higher than
cross-correlation peaks by just 21-24 dB, which can cause false acquisition [Kaplan,
1996]. Table 2.1 lists the C/A-code cross correlation power probabilities.
18
Table 2.1: Cross correlation probability of C/A code [from Kaplan, 1996]
The P-code is not a maximum length sequence but since its period is very long, its
autocorrelation and cross-correlation properties are almost ideal. The cross-correlation
peak between the P-codes is 127 dB lower than the autocorrelation peak, which is much
better compared to 24 dB difference for the C/A-codes [Kaplan, 1996]. The P-code is not
discussed in detail in this thesis since only the C/A-code is used for the research.
2.3 GPS Observations and Error Sources
Three different types of measurement information can be extracted from a GPS satellite
signal, namely a pseudorange measurement, a carrier phase measurement, and the
Doppler measurement.
2.3.1 Pseudorange
A GPS pseudorange measurement is the apparent distance between receiver and satellite
obtained as a difference between transmission and reception time [Leick, 1995]. The term
pseudo comes from the fact that the measured range has an unknown clock bias which
has to be estimated [Misra and Enge, 2001]. GPS measurements suffer from various
errors arising out of clock and other propagation errors as follows:
Cumulative Probability
of Occurrence
Cross correlation for any
two codes (dB)
0.23 -23.9
0.50 -24.2
0.99 -60.2
19
pionotroporb tdTtdtctdtddttp ερ +−++++= ))()(()()()()( (2.7)
where
)(tp is the pseudorange measurement at time t (m),
)(tρ is the true range between satellite and receiver at time t,
)(tdorb is the orbital error,
)(tdtrop is the tropospheric error at time t,
)(tdiono is the ionospheric error at time t,
c is the speed of light (≈ 2.99 x 108 m/s),
)(tdt is the satellite clock error at time t,
)(tdT is the receiver clock error at time t, and
pε is the combined error due to multipath and receiver noise.
Orbital error, satellite clock error, and atmospheric delay are common to standard
and HSGPS measurements. These effects are spatially correlated and can be reduced by
differencing pseudorange measurements with a receiver at a known location (i.e.
differential GPS (DGPS)) or by analytical modeling often based on parameters included
in the broadcast navigation message. The receiver clock error is included as an unknown
parameter in single point and DGPS methods. Noise on the pseudorange measurement
depends on the received signal strength and the correlation method used by the receiver.
Multipath is the result of reflected signals interfering with the direct LOS signal and is a
dominant source of error in GPS methods that utilize the pseudorange measurement.
When no LOS signal is available, measurements are made on multipath signals only.
20
DGPS is normally implemented by differencing the ranges to common satellites from two
receivers. If the coordinates of one station are known, an accurate position of the second
station (rover) can be determined [Misra and Enge, 2001]. DGPS methods does not
reduce multipath, and the noise of a differenced observation is larger than that of an
individual measurement by a factor of √2 [Hofmann-Wellenhof et al., 1994].
2.3.2 Carrier Phase
A carrier phase measurement is a range measurement computed from the GPS carrier
signal information. The total number of the carrier cycles from the GPS satellites to the
user are measured and converted into a range measurement using the carrier wavelength
[Kaplan, 1996]. The receiver cannot determine the number of integer cycles before the
signal is acquired. This is referred to as the integer cycle ambiguity. This ambiguity must
be resolved before the carrier phase measurement can be used for position computation. It
can be represented as follows [Wells et al., 1986]:
( ) θελρλϕφ ++−+−++=−= NtionodttropdtdTtdtcorbdttt )()()()()()()( (2.8)
where
φ (t) is the carrier phase measurement at time t (m),
λ is the carrier wavelength (m/cycle),
ϕ(t) is the carrier phase measurement (cycles),
N is the integer carrier phase ambiguity (cycles), and
εθ is the carrier multipath and measurement noise (m).
21
The definitions of all other symbols in the above equation are the same as in
Equation 2.7. The carrier phase measurement with the ambiguity resolved to the correct
integer provides a very accurate range measurement and is used to provide
centimetre-level position accuracies.
2.3.3 Doppler Measurement
The Doppler effect is the change in reception frequency due to the relative motion of the
transmitter and receiver and is a direct measure of the rate of change of range between the
two points [Ashjaee, 1985]. Thus the Doppler measurement can be used to calculate the
velocity between the transmitter and the receiver. In GPS, Doppler is a measure of the
instantaneous phase rate of a tracked satellite’s signal [Ward, 1996]. Thus, the velocity of
the user with respect to GPS satellites can be determined. The Doppler measurement does
not only include effects due to motion but also the receiver clock drift [Lipp and Gu,
1994]. Doppler observation equation is given as follows:
pionotroporb tTdttdctdtdtdtt ερφ &&&&&&&& +−++++= ))()(()()()()()( (2.9)
where
)(tφ& is the Doppler measurement at time t,
)(tρ& is true geometric range rate between satellite and receiver at time t,
)(tdorb& is the orbital drift error,
)(tdtrop& is the tropospheric delay drift error at time t,
)(tdtrop& is the ionospheric delay drift error at time t,
22
)(ttd& is the satellite clock drift at time t,
)(tTd & is the receiver clock drift at time t, and
pε& is the combined drift error due to multipath and receiver noise.
The effects of troposphere, ionosphere, orbital error and satellite clock drift can
partly be compensated by differencing or by the parameters in the navigation message
[MacGougan, 2003]. The effect of multipath on the Doppler measurement is fairly small
as it is derived from the phase range measurements (which are less effected by
multipath). However the effect of the receiver clock is variable and depends on the
quality of the oscillator used in the GPS receiver. This error is fairly large for low cost
GPS receivers and is commonly estimated as an unknown parameter. Consequently, a
minimum of four Doppler measurements are needed to estimate the user 3D velocity and
receiver clock drift.
2.3.4 Error Sources
GPS measurements have various errors including satellite clock errors, orbital errors,
atmospheric errors, receiver clock error, multipath and interference [Wells et al., 1986].
The satellite clock error is the drift in the satellite clock with respect to the GPS time
reference. The GPS master control station synchronizes the satellite clock with the GPS
clock during the upload of the navigation information, and this offset is transmitted in the
navigation message. The satellite orbital error is the difference between the satellite’s
position using the ephemeris and the actual values [ICD, 2003].
23
When the GPS signal travels through the troposphere, its path will bend slightly
due to the refractivity of the troposphere [Kaplan, 1996]. The change of the refractivity
from free space to the troposphere causes the the GPS signal to slow down which results
in a delay of the GPS signal. This tropospheric delay is a function of the temperature,
pressure, and relative humidity [Spilker and Parkinson, 1996]. Hopfield [1969] and
Saastamoinen [1972] have developed different tropospheric delay models which can
reduce the tropospheric error by about 90%.
The ionosphere is the layer of the atmosphere that extends from about 60 km to
over 1000 km of height above the Earth’s surface. It is a significant source of range and
range-rate errors for GPS users requiring high-accuracy measurements. The ionospheric
variation is generally large compared to the troposphere and is more difficult to model.
The Doppler induced by ionospheric changes is a function of the total electron content
(TEC). A TEC unit represents 1016 electrons per square metre of ionospheric cross-
section [Spilker and Parkinson, 1996]. Ionospheric errors can be eliminated using dual
frequency measurements from GPS. The single frequency ionospheric Klobuchar model
described in ICD [2003] can reduce the ionospheric error by up to 50%. Ionospheric
errors can be further reduced using better ionospheric estimation models. For example,
Wide Area DGPS systems such as Wide Area Augmentation System (WAAS) and
Canada-wide Differential GPS (CDGPS) can be used to provide ionospheric corrections
[Cannon et al., 2004].
24
The user clock is often inaccurate and not synchronized with the GPS clock,
which results in the user clock error. The approximate magnitudes of different errors are
listed in Table 2.2.
All errors except multipath and noise can be reduced using techniques such as
single-differencing, double-differencing and DGPS corrections. As previously
mentioned, multipath is the error caused by the reflected GPS signals entering the
receiver front-end and mixing with the direct signal. Its effect will be more pronounced
for static receivers close to large reflectors. It is specific to a receiver/antenna and
depends on the surrounding environment [Braasch and Van Grass, 1991].
Table 2.2: GPS Error Sources [from Lachapelle, 2002]
GPS Error Source Error magnitude (1 σ)
(m)
Satellite clock and orbital errors 2.3
Ionosphere on L1 7.0
Troposphere 0.2
Code multipath* 0.01-10
Code noise 0.6
Carrier multipath 50x10-3
Carrier noise 0.2-2x10-3
* Outdoor only; may be much larger indoors
Another major source for degradation of the GPS accuracy and reliability is RFI.
Since there are other sources of errors which further degrade GPS accuracy, this makes
RFI mitigation even more difficult. Adding to the problem, GPS satellites and users are
25
mobile which makes it difficult to integrate the signals over long periods of time to
average out the effects of noise. Satellite and user motion introduce Doppler effects, slow
power fluctuations (due to changes in the effective antenna gain and path loss) and fast
power changes (due to multipath fading, blockage and shadowing) [Heppe and Ward,
2003]. A Doppler fluctuation makes it difficult to distinguish between user motion and
receiver clock drift. Power fluctuations make it difficult to determine the thresholds for
acquisition and tracking while atmospheric errors introduce range and range-rate errors.
Two major concerns for providing users with a reliable GPS solution are RFI and
jamming. Unintentional interference can be caused by RF transmitters, harmonics of
ground transmitters, radar signals and accidental transmission of signals in the wrong
frequency band [Spilker and Parkinson, 1996]. These signals, or the harmonics of the
signals, near the GPS frequencies (L1 and L2) are potential sources of interference.
Interference can also be caused by ionospheric scintillation and evil waveforms
transmitted by the GPS satellites themselves [Jakab, 2001; Geyer and Frazier, 1999].
Pulsed interference can result from radar signals in nearby frequency bands which are not
properly filtered [Littlepage, 1999].
Continuous Wave (CW) interference can be either a pure tone or a narrow band
modulated signal such as AM or FM [Macabiau et al., 2001]. It adds to the signal
spectrum and can affect the carrier tracking. Higher order harmonic emissions from AM
and FM radio broadcast transmitters fall close to the GPS L1 frequency and cause
interference. More details about these types of RFI and their affects on GPS signal
acquisition and tracking will be discussed later in Chapter 5.
26
2.4 Receiver Architecture
A conventional GPS receiver consists of three blocks which process the incoming GPS
signal in three different frequency ranges. The RF section operates on the incoming GPS
signals at the GHz frequency range, the signal processing section operates on the signal at
the MHz/KHz frequency range and the data processing section operates at the Hz
frequency range. A conventional GPS Receiver block diagram is shown in Figure 2.3.
The RF section is responsible for receiving the GPS signal from the antenna and
down converting it to an intermediate frequency (IF). The down conversion process can
be performed in a single stage or in multiple stages [Kaplan, 1996]. Each stage consists
of a local oscillator, mixer and band pass filter to eliminate the undesired mixer product.
The RF section amplifies the signal and also determines its precorrelation bandwidth. The
IF signal is sampled at a desired sampling rate using an automatic gain controller (AGC)
and an analog-to-digital converter (ADC) [Tsui, 2000].
Figure 2.3: GPS Receiver Architecture
27
The signal processor acquires and tracks the signals and determines the navigation
data bit value. Acquisition involves performing a two-dimensional search in the code and
Doppler range. It involves a carrier wipe-off wherein the carrier from the incoming GPS
signal is removed and a code wipe-off wherein the PRN code from the incoming GPS
signal is removed. Once the carrier is wiped off, the residual frequency component is the
Doppler. The acquisition process must replicate both the carrier and code of the satellite
in order to acquire it. To acquire the signal, correlation is done over a period called the
predetection integration period, which is chosen depending on the acquisition scheme,
TTFF requirement, data bit prediction and Doppler frequency [Tsui, 2000]. When the
replica signal correctly matches the code and Doppler of the received signal, a GPS
signal peak is obtained. This peak is easily distinguishable from other peaks at the
nominal power (-130 dBm).
Once the signal is acquired, the tracking loops are used to keep lock on the signal
and to detect the navigation data bit transitions. A phase lock loop (PLL) and a
frequency lock loop (FLL) are used to track the carrier signal whereas a delay lock loop
(DLL) is used to track the code phase [Spilker and Parkinson, 1996]. This section
generates the pseudorange and the Doppler measurements, computes the Carrier-to-Noise
(C/N0) ratio of the signal to determine signal quality and determines the thresholds for
acquisition and tracking processes. It also extracts the raw navigation data from the data
bits collected.
Position, velocity and time are computed by the navigation processor using the
raw pseudorange, Doppler and navigation bit stream provided by the signal processor.
28
The navigation processor must decode the 50 Hz navigation data and compute the GPS
satellite positions. With that information, it can then estimate position and time by least-
squares adjustment of the pseudoranges. With accurate position information, Doppler
measurements can be used to estimate velocity precisely. The algorithms and techniques
used in navigation processing vary with each receiver implementation and depend on the
purpose of the receiver. Beyond the typical least-squares approach, some receivers
implement heavy filtering and employ other error detection and mitigation techniques.
Current GPS receivers combine the receiver blocks to reduce cost and size and to
have a greater level of integration. Advances in GPS receiver technology have made it
possible to have a 12-channel receiver with the capability of computing the navigation
information at a 10 Hz rate, being smaller in size than a credit card, and at an affordable
price [Ray, 2003].
Conventional GPS receivers typically use integration times less than the nominal
maximum 20 ms coherent interval and are limited in terms of their operational
environments to where there are strong signals (-130 dBm). In order to extend the
capabilities of GPS into many indoor environments where a receiver has to acquire and
track signals with power levels of -150 dBm or lower, High Sensitivity GPS receivers
were designed.
2.5 High Sensitivity GPS
The navigation data bit duration puts a limit on the coherent integration period. This limit
puts a constraint on the processing signal gain in the acquisition process which
29
determines the GPS signal level that can be acquired [Ward, 1996]. To acquire weak
signals, the predetection integration time has to be extended beyond 20 ms. This can be
achieved by performing coherent integration for 20 ms and non-coherent integration for
the desired duration [Choi et al., 2002]. Non-coherent integration squares and sums the
signal across the coherent integration periods. This allows for a coherent integration time
to be less than 20 ms and a predetection integration time beyond 20 ms. The total gain
using coherent and non-coherent accumulation is given by:
SQlossMTB preGtot −+= )log(10)*log(10 (2.10)
where
Gtot is total processing gain (dB) with respect to pre-correlation SNR
Bpre is pre-detection bandwidth (Hz)
T is total coherent integration time (ms)
M is number of non-coherent accumulations of the coherent output,
and
SQloss is squaring loss due to non-coherent accumulation (dB).
The limitations of coherent correlation accumulation are data bit transitions and
residual frequency errors. Predicting the data bit transitions and limiting residual
frequency errors during coherent correlation is necessary to obtain the optimal gain prior
to non-coherent accumulation. This is because reduction of the ensuing squaring loss is
paramount to successful non-coherent accumulation.
30
The processing gain example for a high sensitivity receiver is given in Table 2.3.
For a high-sensitivity GPS receiver, the desired acquisition sensitivity is -150 dBm or
lower.
Table 2.3: Processing Gain Example
For a -150 dBm GPS signal with an IF bandwidth of 2.046 MHz, the IF SNR is
about -40 dB. After accounting for implementation losses from 2-bit quantization and
Signal strength at Antenna -150.0 dBm
IF Bandwidth 2.046 MHz Teff 363.0 Kelvin
Noise power -109.2 dBm 10 log (k*Teff * BW) where k is Boltzmann’s constant k = 1.3806503 × 10-23 m2 kg s-2 K-1
IF SNR -40.1 dB Signal Strength (dBm) - Noise Power (dBm)
Coherent addition Pre-detection
bandwidth (Bpre) 2.046 MHz
Coherent interval (T) 20.0 Ms Length of coherent integration Coherent gain (dB) 46.1 dB 10 * log ( Bpre * T) = 10 log (2046 * 20)
Implementation losses 2.0 dB 1.2 dB for 2-bit quantization, 0.5 dB due to filter and 0.3 dB for other losses
Actual coherent gain 44.1 dB Perfect coherent gain - implementation losses
SNR after coherent addition 4.0 dB IF SNR + actual coherent gain
Non-coherent (NC) addition Squaring loss 3.8 dB From Squaring Loss
Total integration period 1,000 milliseconds Total integration time = Number of NC sums (M) * coherent interval
Number of NC sums, M 50.0 coherent
intervals integration period/length of coherent interval
Non coherent gain (dB) 17.0 dB = 10*log10(M)
Expected Output SNR (dB) 17.2 dB coherent SNR + non coherent gain -
squaring loss
31
filter loss, 20 ms of coherent integration will increase the SNR to about 4 dB. To detect a
signal, one requires a SNR of at least 14 dB, so non-coherent accumulation is needed. In
this case, the total integration period is 1 second, which means 50 non-coherent
accumulations are performed to provide an extra non-coherent gain of about 17 dB. The
final expected SNR is about 17 dB, which is higher than the required 14 dB to detect a
signal.
As stated previously, non-coherent integration requires the coherent integration
values and consequently, the noise increases. This is called the squaring loss. The
squaring loss depends upon the SNR after coherent integration and before non-coherent
integration [Ray, 2003]. This loss can be reduced by multiplying the adjacent coherent
integration samples over the desired period [Chansarkar and Garin, 2000]. Lin et al.
[2002] proposed an incoherent integration scheme to reduce the squaring loss present in
non-coherent integration. In this scheme, the absolute amplitudes of the coherent
integrations are summed up instead of squaring before summation which reduces
squaring loss. Multiple thresholds for detection with different coefficients based on the
false detection probability were chosen to compensate for the power loss during the
correlation due to the Doppler frequency mismatch and the code phase transition.
The ability to acquire and track weak GPS signals depends on the capabilities of
the receiver to maximize the coherent integration interval prior to non-coherent
accumulation while minimizing residual frequency errors during coherent integration. In
addition, the design of the receiver must also minimize the impact of thermal noise to
maintain signal tracking. The ability to predict the sign of the bits and the timing of the
32
navigation message signal modulation directly affects the ability to perform long
coherent integration. Thermal noise also induces frequency error jitter depending on the
carrier tracking loop bandwidth. Thermal noise can often be a dominant source of carrier
tracking error, especially for weak GPS signal tracking [MacGougan, 2003].
Residual frequency errors during coherent integration cause a reduction in the
coherent signal gain and higher squaring loss for non-coherent accumulation. Residual
frequency error sources include oscillator instability and user motion-induced Doppler
effects. An error in the receiver clock frequency manifests as a measured Doppler effect
as well as any phase noise in the clock signal makes the Doppler appear to change
rapidly. The receiver clock stability depends on the external oscillator parameters and the
frequency synthesizer used within the Digitizer [Watson, 2005]. A high stability clock
provides less jitter and variations in the Doppler and code measurements, thus enabling
edge-to-edge correlation. A stable clock also enables longer pre and post detection
integration to be performed on the signal without loss of signal due to drift [Sudhir et al.,
2001].
In general, high sensitivity methods can be implemented in either aided or
unaided modes. In unaided mode, the high sensitivity receiver lacks the ability of the
aided receiver to acquire weak signals if it has no a priori knowledge about GPS time and
the current position. However, if an HSGPS receiver is initialized with assistance data by
first acquiring and tracking four or more GPS satellites with strong signals, it has the
same functional capability as an AGPS receiver as long as it can maintain timing,
approximate position, and satellite ephemeris. In aided mode (AGPS), initialization (the
33
need to acquire and track four satellites in a strong signal environment) is unnecessary as
the network provides the assistance data.
2.6 Assisted GPS
The idea of providing assistance to a GPS receiver is not recent. Taylor and Sonnet
[1981] proposed transmitting an acquisition-aiding signal generated by an earth-based
control station to user terminals via a geostationary satellite to simplify user equipment.
The aiding signal identifies satellites in view having best geometry and includes Doppler
prediction data as well as GPS satellite coordinates and identification data associated with
user terminals within an area being served by the control station. As a result, the concept
of aiding has been applied in many variations for almost quarter of a century [van
Diggelen, 2001b].
In present systems, AGPS requires a server with a reference GPS receiver that has
clear LOS views of available satellites. The server collects satellite almanac, ephemeris,
approximate user position (which is defined by the initial reference position and its
associated uncertainty) and timing assistance data from the reference receiver, computes
the assistance information and sends the assistance information to the rover receiver. The
receiver, which typically resides in the mobile cellular handset, uses this information to
speed up the acquisition process (see Figure 2.4).
AGPS methods can be divided into two categories, depending on where the user
GPS position calculation is performed. If the position is calculated at the user, called the
Mobile Station (MS), the method is called MS-based GPS. Alternatively, if the network
34
calculates the position, it is called MS-assisted GPS [Syrjärinne, 2001a]. MS-based GPS
requires assistance data from the network prior to position computation in the handset.
MS-assisted GPS is a distributed system where the function of the handset is to compute
the pseudorange or coarse position and send this information to the network (server),
which then computes the MS position. MS-assisted GPS has the significant downside on
being totally dependent on network coverage and channel capacity [Syjarinne, 2001b]. If
no assistance is available from the network, AGPS architecture could allow the GPS
receiver to work in standalone mode. However, in this case, the acquisition sensitivity of
the receiver would be lower.
Figure 2.4: AGPS System
35
In principle, the assisting network does not necessarily have to be a cellular
network. It is equally possible to transmit the latest navigation data and time assistance,
for example, through a Blue Tooth-connectivity layer or a Wireless Local Area Network
(WLAN). However, currently, the assistance usually comes from a cellular network. For
example, Global System for Mobile Communications (GSM) standards already support
the delivery of assistance and the major cellular manufacturers are committed to make the
necessary modifications for AGPS [Syrjärinne, 2001a].
Both the CDMA and the GSM communities have developed standards for
Network Plane AGPS messaging. The standards are outlined in TIA/EIA/IS-801-1 for
CDMA networks while the GSM standards are outlined in 3GPP2 C.S0022-0-1 and
3GPP TS 25.331. The minimum operational performance for AGPS handsets are
presented in TIA 916 for CDMA and 3GPP2 C.P9004-0 and 3GPP TS 25.171 V6.0.0 for
GSM networks [Bryant, 2004]. There is considerable similarity between the assistance
fields included in the two protocols. In addition, for both networks, the minimum
performance standards are tested using statistical testing for five tests.
The five tests include sensitivity, nominal accuracy, dynamic range, multipath
scenario and moving scenario with a periodic update. The nominal accuracy tests are for
static accuracy under typical signal strength conditions rather than weak signal conditions
and with no multipath present. The performance in the presence of multipath is tested
separately as are the performances under weak signal conditions and under typical land-
based dynamic conditions [ibid].
36
2.6.1 Aiding Parameters
The network can provide various types of assistance data including ephemeris, almanac
and approximate user position. The ephemeris is valid for up to four hours and can take
up to 30 seconds to download a satellite’s ephemeris in a conventional GPS receiver. By
providing the ephemeris from an external source, an AGPS-capable mobile handset can
focus on acquisition and position computation earlier, which leads to a much lower
TTFF.
The almanac can be used to predict the approximate location of a satellite and is
typically used when the ephemeris is not provided. The Almanac coefficients remain
reasonably accurate for months. There are only 10 coefficients per satellite and each of
these consists of fewer bits than the corresponding Ephemeris coefficients. Each satellite
transmits the Almanac coefficients for the entire constellation. The clock corrections are
also included. However, the Almanac coefficients are intended only to provide coarse
satellite location suitable for determining which satellites are visible and the approximate
signal Doppler frequency offsets [Bryant et al., 2001].
Another parameter the network can provide to an AGPS receiver is the position of
the reference station receiver. It is assumed that the rover/user receiver is within 100 km
of the reference station and therefore, both receivers have roughly the same visible
satellites. From this information, an AGPS receiver (i.e. a mobile cellular phone user) can
determine the satellites in view [Garin et al., 1999].
To achieve rapid positioning, the range of frequency uncertainty in acquiring the
satellites at the client receiver must be reduced as much as possible in order to reduce the
37
search time. For this reason, it is common practice in AGPS systems for the server to
transmit Doppler information to the client receiver. Even with this information, the
frequency uncertainty of the receiver local oscillator still remains as an obstacle to rapid
acquisition. Today’s technology can produce oscillators which have a frequency
uncertainty on the order of ±1 part per million (ppm) at a cost low enough to permit
incorporation into a consumer product such as a cell phone [Weill et al., 2004]. But even
with such an oscillator, 1 ppm translates into about ±1575 Hz of frequency uncertainty at
the GPS L1 frequency. Assuming that the coherent integration time during satellite
searching is 20 milliseconds (the length of a navigation message data bit), the frequency
bins in the search would have a 50 Hz spacing. This means, a total of 2 × 1575/50 = 63
frequency bins might have to be searched to find the first satellite. Once the first satellite
is acquired, the local oscillator offset can be determined, and the frequency uncertainty in
searching for the remaining satellites can thereby be reduced [ibid].
The received GPS signals are shifted in frequency due to the relative receiver-
satellite motion which is the so-called Doppler frequency shift. The receiver must find the
frequency of the signal before it can lock onto it. Knowledge of the satellite position and
velocity data and the approximate receiver position reduces the number of frequency bins
to be searched because the receiver directly computes the Doppler frequency shift instead
of searching over the entire possible frequency range. By giving the receiver ephemeris,
almanac and approximate user position, and controlling the local oscillator drift by
synchronizing with a network, the search space is reduced corresponding to the
38
calculated satellite Doppler frequency. Reducing the number of frequency bins which
must be searched to acquire the signal reduces the TTFF.
2.6.2 Timing Information
Prediction of the data bits for enabling coherent integration up to 20 ms or for data wipe-
off (coherent integration longer than 20 ms by cancelling the sign of the incoming bits)
requires precise timing [MacGougan, 2003]. If the GPS receiver knows the absolute time,
it can also find out the exact period of a certain satellite's C/A code. Since the GPS
receiver knows what kind of codes the satellites are sending, it can begin to search for the
right period of the code. On its own, the absolute time does not help the GPS receiver
significantly. A rough estimate of the GPS receiver's position and the position of the
satellites are also needed. An accurate enough position for the MS can be achieved using
GSM Location Services such as Enhanced Observed Time Difference (EOTD). MS
position and satellite positions are needed to remove the transfer delays from the satellite
to the MS, which are also used in solving the period of the satellite code [Kinnari, 2001].
As mentioned previously, a C/A code search has two variables; code phase and
Doppler frequency. Without any assistance data, the Doppler frequency uncertainty
sequence is about 12 kHz. This uncertainty depends on three factors: satellite Doppler
error contribution (±4.5 KHz), local oscillator drift which introduces about ±1.5 KHz
assuming 1 ppm oscillator and user Doppler uncertainty, which is ±300 Hz [Kubrak et
al., 2004]. The C/A code length is 1023 chips and the code phase is usually searched in
0.5 chip increments. The combination of one code bin and one Doppler bin is referred to
39
as a cell. In acquisition, all these cells have to be swept through a few times to remove the
possible error due to noise peaks. With assistance data, the number of possible cells can
be decreased. The satellite's Doppler uncertainty is received in the assistance data and,
thus, the searched Doppler frequencies can be reduced [Kinnari, 2001].
With an accurate reference position and accurate time, the searched code phase
can also be decreased to a few chips. If reference position is known with the uncertainty
of a few hundred metres and the time uncertainty is less than 10 µs, the amount of
searched chips can be decreased to 10 chips. This means that only half of the frequencies
and about one hundredth of the chips have to be searched. With this assistance
information, the total number of the searched cells can be decreased significantly. When
the GPS receiver has synchronized to the first satellite, the clock error of the assisted time
can be solved. The synchronization of the other satellites can be done even more
accurately because the exact time is now known. The GPS receiver knows the exact C/A
code phase where the synchronization should be found [Syrjärinne, 2001b].
Depending on the accuracy of the assisted time, the width of the searched C/A
code window in acquisition can be chosen. The aim of the C/A code search is to get the
GPS receiver synchronized to the C/A codes sent by the satellites. With accurate assisted
time, the searched C/A code window can be narrowed. If the assisted time is not accurate
enough, it has no use in acquisition and the whole C/A code has to be searched. Because
of the correlator implementation used in the GPS receiver, there are a few reasonable
widths of the searched C/A code window, which define a few boundary values for the
accuracy of the assisted time.
40
The optimum target of the time accuracy is that the assisted time error is less than
10 µs. In this case, the search for the synchronization of the incoming code can be started
from the assumed code phase. Now there is no need to do any sweep over the whole code
sequence and no satellite acquisition is needed. The value, 10 µs, for the time accuracy is
due to the width of the correlator. The width of the correlator used is ten chips and, thus,
ten chips can be correlated at the same time. The frequency of the C/A code is
1.023 MHz, which means that the period of one chip is approximately 1 µs and the period
of ten chips is approximately 10 µs. If the error is less than that, the searched C/A code
phase is still in the correlator and it can be found at once. The reference position received
from the LMU must also be quite accurate. The reference position error may not be more
than a few hundred metres [ibid].
In a non-synchronized network, such as GSM, the handset needs to treat the
supplied code phase measurements as relative code phase offsets. In order to do this, the
handset locks on to the first satellite using its Doppler information and a potentially large
search in the time (code phase) domain. Once it locks onto the first satellite, it calculates
the difference between the code phase measurement for this satellite and that supplied in
the assistance data. This offset is then applied to all of the other code phase estimates in
order to determine where to search for those satellites. In a non-synchronized network, it
will take a longer time to lock on to the first satellite but once it gets that one, the narrow
code phase search using the assistance data can be applied to the other satellites [Harper
et al, 2004].
41
2.6.3 AGPS Field Tests
As mentioned in Chapter 1, AGPS field tests have been performed by many GPS
companies. SnapTrackTM, now a QualComm Company, performed tests in various
environments to test the SnapTrack server-aided GPS architecture and Digital Signal
Processing (DSP) software-based receiver solution. The position accuracy ranged from
four metres for the outdoor open site test to 84 m for a 50-story glass/steel building. The
receiver provided position results from 89% (for the glass/steel building test) to 100% of
the time for the outdoor test as well as inside a sport utility vehicle [Moeglein and
Krasner, 1998].
Field tests have also been carried out by SiRF Technology Inc. using SiRFLocTM
client, which is a multimode GPS receiver able to work with assistance data or in
standalone mode. The test was carried in several locations: in a parking lot, in a narrow
walkway between tall buildings, a shopping mall with a glass roof, a two-story office
building and a restroom inside a two-story office building. These tests were carried out in
MS-based GPS mode and the RMS error was around 100 m with the exception of the
narrow walkway where it was 183 m [Garin et al., 1999]. Tests performed by Garin et al.
[2002] found that for weak signals with a C/N0 of 23 dB-Hz, the TTFF was
approximately 40 seconds while the TTFF was about five seconds for open sky
conditions.
Global Locate Inc. also carried out AGPS field tests using the GL-16000TM in
downtown San Francisco. These tests were done inside a shirt pocket, inside a steel truck
going at 112 km/h, a parking lot and a shopping mall. On the bottom floor of the parking
42
lot (four floors from the top), the receiver was able to acquire signals between -150 dBm
to -155 dBm [van Diggelen, 2001b].
43
CHAPTER 3: Acquisition and Tracking Tests
This chapter discusses acquisition and tracking tests performed to obtain the sensitivities
of AGPS, HSGPS and conventional GPS receivers. All of the tests were performed using
a hardware GPS simulator, which is described in the following section.
3.1 Hardware GPS RF Simulation
When testing, it is valuable to have conditions whereby the environment is controllable.
This is very difficult, if not impossible, with field testing. Other advantages of using
simulators are that it is easier to isolate the variables of interest, and results can be
verified with multiple tests that assess repeatability. In recent years, advances in
simulation technology have contributed to the development of state-of-the-art hardware
GPS RF signal simulators. The system at the University of Calgary is the GSS6560,
which is comprised of a control computer and two synchronous 12-channel L1 C/A code
hardware signal simulation units [Boulton, 2002]. These units, shown in Figure 3.1, will
be referred to as simulator vehicles throughout this thesis. The simulator allows real-time
control of the signal level (±20 dB with respect to -130 dBm) for each satellite
corresponding to one channel of signal [ibid]. Some of the simulator capabilities are as
follows:
• Control of the signal power for each channel
• Satellite constellation definition and modeling
• Atmospheric effects modeling (ionospheric/tropospheric)
• Vehicle motion modeling for aircraft, cars, and spacecraft
44
• Simulated vehicle trajectories
• Multipath simulation
• Antenna gain pattern manipulation
• Terrain obscuration modelling
Figure 3.1: Spirent GSS6560 Hardware Simulator
The GSS6560 can be combined with the GSS4766 Interference Simulation to test
satellite navigation equipment in the presence of intentional or unintentional RF
interference. The GSS4766 is capable of a large power and frequency range, multiple
operating modes and multiple channel configurations. It has the following characteristics:
• Broad range of signal options to simulate many types of sources
• CW, AM, FM, and variable bandwidth noise (optional) signals available.
• Pulsed mode available for CW and Noise.
45
• Large power range
• Multi-channel configurations available
• Interactive and modelled modes of operation
The GSS4766 Interference System was used to conduct the RFI tests, which are
described in Chapter 5.
3.2 Test Setup
The tests performed in this section use the SiRFLocTM AGPS evaluation kit. However,
for comparison purposes, two other receivers are also used. All three receivers used are
described below:
• SiRFLocTM – This is a 12-channel L1 C/A-code AGPS receiver. Aiding
information is provided via another receiver, referred to as the Time Transfer
Board (TTB). The TTB acts as the reference receiver and can provide assistance
data including timing and reference position and associated uncertainties with the
reference initial position. The TTB employs a temperature compensated crystal
oscillator (TCXO) to provide a frequency accuracy of ±0.5 ppm. For more details
about the SiRFLoc system architecture, refer to Garin et al. [1999].
• SiRFXTracTM – This is an HSGPS receiver which is similar to the SiRFstarII but
with improvements made in acquisition sensitivity. For the XTrac, more non-
coherent integration is used in acquisition. In tracking mode, 10 ms integration is
performed with the StarII while 20 ms of integration is performed with the XTrac
[Cox, 2005]
46
• NovAtel OEM4 – This is a 24-channel L1/L2 dual frequency geodetic receiver
which is optimized for high accuracy performance under normal operating
conditions.
The expected acquisition sensitivities of the receivers are as follows: -150 dBm
for the AGPS and -142 dBm for the XTrac [SiRF, 2005]. The OEM4 is not expected to
acquire much below nominal signal strengths and the expected tracking threshold for this
receiver is -139 dBm [MacGougan et al., 2002]. The AGPS and HSGPS receivers are
expected to track down to at least -156 dBm, which was the value reported for the
SiRFstarII [MacGougan, 2003].
The test setup used for acquisition and tracking tests conducted is shown in
Figures 3.2 and 3.3.
Figure 3.2: Schematic of Simulator Test Setup
47
Figure 3.3: Test Setup Components
The setup for both the acquisition and tracking tests is similar. The only
difference is that for the acquisition tests, all the receivers are set to a cold start mode at
each power level. In cold start mode, the receiver has no acquisition aiding information
available, meaning it has no information about the current time, the orbits of the satellites
or its current position; therefore, it has to perform a full search to acquire available
satellites.
However, for tracking, all receivers are initialized at -130 dBm and then the
power level of each satellite is lowered until no position fix is obtained. No cold starts are
performed when the power level is lowered.
48
The Low Noise Amplifier (LNA) provides a 30 dB gain while also setting the
effective noise floor for the receivers under test. The noise figure of the LNA indicates
the amount of noise power the LNA will contribute to the total receiver noise. The more
noise the LNA contributes, the higher the noise floor and less sensitive the receiver. The
noise figure is usually expressed in decibels (dB), and is with respect to the thermal noise
power at the system impedance, at a standard noise temperature (usually 20o C, 293 K)
over the bandwidth of interest. It is determined by measuring the ratio, usually expressed
in dB, of the thermal noise power at the output, to that at the input, and then subtracting
from that result, the gain, in dB, of the system. Typical noise figures range from 0.5 dB
for very low noise devices, to 4 to 8 dB.
The 5 dB attenuator provides the LNA with burnout protection. A 10 dB
attenuator was inserted prior to the LNA in order to provide signals as low as -160 dBm.
All hardware simulation tests are designed with 10 to 12 satellites in simulation with no
orbital, atmospheric, multipath or any other errors. Only the effect of noise on receiver
performance will be seen. This is important because it represents only errors due to weak
signal conditions which is what is been investigated. However, this means the errors will
be less than what is expected of a real-life scenario since multipath will produce
significant errors indoors and in an urban canyon environment.
It should be noted that although the AGPS receiver is cold started for each
acquisition test trial, the TTB provides assistance data to the AGPS receiver at the
beginning of each trial, after the cold start has been performed. The cold start is done so
that the procedure is consistent for all of the receivers tested.
49
3.3 Acquisition
Acquisition tests were performed under two scenarios, (1) all satellites having the same
power which is decreased incrementally, and (2) one satellite having a strong signal while
the signal strengths for the remaining satellites are decreased incrementally. The two
scenarios were tested for two reasons. In some weak signal environments, it is possible
that one strong signal may be present. For example, in an indoor environment, there may
be a window which permits at least one strong signal. When a GPS receiver has
synchronized with the first satellite, the clock error of the first satellite can be solved.
With this information, synchronization with other satellites can be done even more
accurately since the GPS receiver knows the exact C/A code phase where the
synchronization should be found, assuming the position is known within a certain
accuracy [Kinnari, 2001]. A more accurate frequency search window can be defined from
the first satellite as it will assist in narrowing down the clock bias. The AGPS receiver
under test has implemented these techniques. Therefore, it is necessary to test the
acquisition sensitivity under both scenarios.
3.3.1 All Satellites with the Same Power
For this test, the power level of each satellite in simulator vehicle 1, which is connected
to the rover receiver (AGPS receiver), was decreased by 1 dB, starting from -130 dBm
and continuing until the receiver could no longer obtain a position fix. Simulator vehicle
2 was connected to the TTB, which is the reference receiver, and the power level of this
vehicle was kept at -130 dBm throughout this test. This is because in reality the reference
50
receiver will reside at the base station and is assumed to operate under good signal
conditions.
At each power level, at least twenty trials were conducted from a cold start. A
trial is considered to be an acquisition from a cold start followed by five position fixes.
To obtain a position fix, the receiver is required to acquire and track at least four
satellites. The receivers were given five minutes to acquire the signal, meaning a position
fix must be obtained within 5 minutes in order for a trial to be successful. The elevation
mask was set to 5° for all the receivers. A timing accuracy of 125 µs was provided to the
SiRFLoc AGPS receiver by the TTB. In real-life AGPS implementations, the timing
assistance can vary from a few microseconds to a few seconds [Bryant, 2004]. The
horizontal uncertainty was set to 2 km while the vertical uncertainty was 50 m.
The acquisition performance threshold when all the signals have the same power
is shown in Table 3.1. The SiRFLoc AGPS receiver was able to acquire and obtain a
position fix when the power level was above -145 dBm while the HSGPS SiRFXTrac
receiver was able to acquire above -140 dBm. The OEM4 was only able to acquire until a
power level of -133 dBm.
Table 3.1: Acquisition Performance (All Same Power)
Acquisition Threshold (dBm) Receiver
Min Max Mean SiRFLoc (AGPS) -145 -145 -145 SiRFXTrac (HSGPS) -140 -139 -140 NovAtel OEM4 -133 -132 -133
51
In this scenario, the AGPS receiver is 5 dB better than an HSGPS receiver and
12 dB better than a conventional receiver. The results are as expected; the OEM4 is not
expected to acquire weak signals while the acquisition sensitivity of the XTrac is close to
the specifications.
The average normalized TTFF for the AGPS receiver when all of the satellites
had the same power is shown in Figure 3.4. In the context of this figure, normalized
means that all TTFF values were divided the maximum TTFF value of the acquisition
tests, which was yielded when the power level of all the satellites was -145 dBm. From
the figure, it can be seen that the TTFF remains relatively the same until -136 dBm. For
power levels below -142 dBm, the TTFF increases significantly. This may be due to the
concept of “acquisition maps” that is employed in the AGPS receiver. Different signal
strength ranges have different coherent and mostly, non-coherent integration times as
well as different acquisition strategies may be used depending on the signal strength
[Cox, 2005]. One can assume that the -142 dBm to -145 dBm may fall under the weakest
signal strength range for the AGPS receiver and the combination of integration times and
acquisition strategy may increase the TTFF significantly.
52
Figure 3.4: TTFF for AGPS for 125 µs Timing (All Satellites Same Power)
At the bottom of Figure 3.4, the percentage of position fixes is shown. From the
nominal power level up to -143 dBm, all trials resulted in a position fix; the success was
100%. However, at -145 dBm, which is the acquisition threshold of the AGPS receiver
53
with all satellites having the same power, the percentage of position fixes was
considerably lower, approximately 35%. At -146 dBm, the failure rate was 100%.
3.3.2 One Satellite with a Strong Signal
This test is similar to the above test with one minor modification. One of the satellites in
vehicle 1 was always kept at -130 dBm while the remaining satellites were decreased by
1 dB. In this scenario, the highest elevation satellite was chosen as the strong signal
satellite. Once again, the signals for the TTB were kept at -130 dBm. At each power
level, at least twenty trials were conducted from a cold start. Table 3.2 shows the
acquisition performance with one strong signal.
Table 3.2: Acquisition Performance (One Strong Signal)
Receiver Acquisition Threshold (dBm)
SiRFLoc (AGPS) -153
SiRFXTrac (HSGPS) -140
NovAtel OEM4 -133
With this particular test, the AGPS receiver was able to acquire and obtain a
position fix down to -153 dBm while no change in performance was noticed with any of
the other receivers. As mentioned previously, this improvement is achieved based on a
new signal processing architecture implemented in the SiRFLoc product. The acquisition
sensitivity under the one strong signal scenario is similar to the one found in the
specifications, which specifies the acquisition sensitivity of the SiRFLoc receiver as
54
-152 dBm. Overall, a 13 dB improvement was noticed with an AGPS receiver compared
to an HSGPS receiver such as the SiRFXTrac and a 20 dB improvement over a
conventional receiver in acquisition sensitivity.
The average normalized TTFF for the AGPS receiver with one strong signal is
shown in Figure 3.5. Here, the normalization was once again performed by dividing all
TTFF values by the TTFF when the power level of all the satellites was -145 dBm (from
the previous acquisition scenario since that yielded the maximum TTFF value). From the
figure, it can be seen that the TTFF remains relatively the same until -148 dBm. For
power levels below -151 dBm, the TTFF increases significantly. It can be concluded that
there is a significant benefit from the acquisition of the first satellite in an AGPS
implementation.
At the bottom of Figure 3.5, the percentage of position fixes is shown. A fix is
considered to be a position solution with at least four satellites. For power levels between
-130 dBm and -151 dBm, the AGPS receiver is able to provide a position fix 100% of the
time. However, at -153 dBm, which is the acquisition threshold of the AGPS receiver
with one strong signal, a position fix was obtained less than 10% of the time. Since the
acquisition sensitivity of the AGPS receiver is specified as -152 dBm by SiRF
Technology Inc., this result is not surprising. At -154 dBm, no position fixes were
obtained.
55
Figure 3.5: TTFF for AGPS 125 µs Timing Accuracy (One Strong Signal)
56
3.4 Tracking
A tracking test was performed to investigate the tracking threshold of three different
receiver technologies: AGPS, HSGPS and a conventional GPS receiver. At the start of
the tracking test, the receivers were given -130 dBm signals for 20 minutes. This was
done to make sure that all of the receivers were able to acquire and track the signals.
Then the power level of the satellites in simulator vehicle 1 was decreased by 1 dB every
one minute. No cold starts were performed when the power level was lowered. The
signals from simulator vehicle 1 were directed to all three receivers via a signal splitter
while the simulator vehicle 2 was connected to the TTB and was kept at -130 dBm for the
entire test. The TTB provided 125 µs timing to the AGPS receiver.
The tracking thresholds for the three receivers under test are shown in Table 3.3.
As can be seen, the tracking performance of the AGPS receiver was similar to that of the
HSGPS receiver. As expected, the OEM4 did not perform well under signal degradation
and was only able to track signals as low as -140 dBm, 15 dB less than all other receivers.
Table 3.3: Tracking Performance
Receiver Tracking Threshold (dBm)
SiRFLoc (AGPS) -155 SiRFXTrac (HSGPS) -155
NovAtel OEM4 -140
C/N0 is the best measurable value of the signal quality present at the input to a
GPS receiver. The C/N0 is an instantaneous measure of the ratio of the carrier power
present to noise power density measured per Hertz of bandwidth. With a minimum
57
guaranteed LOS GPS signal power at -130 dBm, the nominal C/N0 level is 44 dB-Hz
[Lachapelle, 2003]. Theoretically, C/N0 is not dependent on the receiver used; however,
each receiver must compute its own C/N0 value based on the measured signal. This
explains why the C/N0 values are different for each receiver (see Figure 3.6).
The raw pseudorange data was extracted at 1 Hz from each receiver and
processed using the University of Calgary’s C3NAVG2TM software [Petovello et al.,
2000]. C3NAVG2 uses a least-squares algorithm to estimate epoch-to-epoch positions,
which is suited to analyze the impact of errors on positions and velocities since no
filtering is performed. The processed data was used to obtain the position errors for all
receivers under test. It should be noted that all position results presented in this chapter
have been post-processed using the C3NAVG2 software.
Differential positioning methods were not employed; only single point positioning
was used. It should be noted that the position results will be optimistic since only noise
was simulated. No additional errors such as orbital errors, atmospheric effects and
multipath were added to the simulation.
The tracking results, in terms of average C/N0 for all satellites tracked, and the
associated simulator channel power, are shown in Figure 3.6 while Figure 3.7 gives the
number of satellites tracked. The tracking test position errors are shown in Figure 3.8.
58
Figure 3.6: Average C/N0 (for all satellites) during Tracking Test
Table 3.4 shows the results for the average number of satellites tracked for four
power intervals. Similarly, the 2D and 3D errors for all receivers for the same four
intervals are shown in Table 3.5. The four intervals were chosen to be representative of
different power levels. The first interval, -130 dBm, is nominal GPS signal power. The
second interval, from -130 dBm to -140 dBm represents the interval where even the
conventional receiver was able to track. The third interval, from -140 dBm to -150 dBm
is indicative of a weak signal environment. The fourth interval, which is below
59
-150 dBm, represents the most difficult conditions for a receiver since the signals are at
least attenuated by 20 dB.
Table 3.4: Average Number of Satellites Tracked
No. of Satellites Power (dBm)
AGPS XTrac OEM4
-130 7.8 7.9 8.7
-130 to -140 7.9 7.9 8.7
-140 to -150 6.1 5.9 N/A
-150 to -155 4.9 4.8 N/A
Figure 3.7: Number of Satellites Tracked during Tracking Test
60
Table 3.5: 2D and 3D RMS Errors
AGPS XTrac OEM4
RMS Error (m) Power (dBm)
2D 3D 2D 3D 2D 3D
-130 1.1 1.6 1.1 1.7 0.3 0.5 -130 to -140 1.3 1.9 1.3 2.1 0.5 0.7 -140 to -150 5.3 8.0 6.0 8.8 N/A N/A -150 to -155 37.7 53.6 24.7 31.6 N/A N/A
Figure 3.8: Tracking Test – Position Errors
61
It is clear from Table 3.5 that the OEM4 provides very accurate position
solutions. This is expected as it is optimized for high accuracy performance. Its 2D and
3D errors are significantly lower than any of the other two receivers during the -130 to
-140 dBm interval. However in weak signal environments (below -140 dBm), it is not
able to track at all which shows the trade-off between accuracy and tracking capability.
The OEM4 also had much better availability than the AGPS and HSGPS receivers. The
number of satellites tracked by AGPS and HSGPS receivers was very similar for all four
power intervals. As previously stated, no additional errors were simulated; the position
results are all due to noise. Therefore, the position results are very optimistic relative to
those expected under field conditions.
The 2D and 3D errors for the HSGPS and AGPS receivers are similar until a
power level of -150 dBm. In fact, as can be seen from Table 3.5, the AGPS and the
HSGPS receivers had very similar availability and position errors throughout the tracking
test. It can be concluded that aiding provides “coarse” estimates intended to assist
acquisition and this “coarse” assistance is not useful in improving tracking performance
because when the receiver is in tracking mode, it provides a much more precise GPS time
and location. Another reason for the similar performance between the AGPS and
HSGPS while tracking is due to the fact that the SiRFLoc (AGPS) and the SIRFXTrac
(HSGPS) receivers from SIRF Technology Inc. have very similar architectures in terms
of tracking; both receivers are based on the SiRFstarII architecture [Turetzky et al, 1999].
The main difference is that the AGPS receiver is capable of accepting assistance data so
one would expect improvements only is acquisition.
62
3.5 Chapter Summary
In this chapter, acquisition and tracking tests were performed to analyze the performance
of the AGPS receiver. Overall, a 13 dB improvement was observed with an AGPS
receiver compared to an HSGPS receiver such as the SiRFXTrac and a 20 dB
improvement over a conventional receiver in acquisition sensitivity. No improvement
was noted in tracking performance between the AGPS and HSGPS receivers. This is
expected since aiding provides “coarse” estimates intended to assist acquisition and this
“coarse” assistance is not useful in improving tracking performance. In addition, both
receivers are based on the SiRFstarII architecture.
63
CHAPTER 4: Assistance Data
In this chapter, the effect of timing accuracy, initial position and the position uncertainty
on AGPS receiver performance are investigated.
4.1 Timing Accuracy
As previously stated, the accuracy of the code phase prediction is directly proportional to
the accuracy of timing assistance. In order to investigate the effect of timing accuracy on
AGPS acquisition performance, a test was designed with 10 satellites in simulation with
no orbital, atmospheric or any other errors. Simulator vehicle 1 was connected to the
AGPS receiver and the power level of each satellite was decreased by 2 dB, starting from
-130 dBm. At each power level, at least 30 trials were conducted. A trial is considered
to be an acquisition from a cold start mode, followed by five position fixes. The receiver
is then cold started to start a new trial.
The elevation mask of the AGPS receiver was set to 7.5°, which is the default
value. Data on the AGPS receiver was collected at least 90 seconds after the TTB was
initialized to ensure that the reference receiver (TTB) was able to get a position fix with
at least seven satellites. The TTB, which was connected to simulator vehicle 2, was given
“strong” signals, -130 dBm. The test setup for all of the tests performed in this chapter is
shown in Figure 4.1. The precise timing accuracy provided by the TTB to the AGPS
receiver was set to one of three levels: 125 µs, 250 µs and 500 µs.
64
Figure 4.1: Test Setup for Timing and Assistance Data
The effect of timing assistance on the TTFF is shown in Figure 4.2. All TTFF
values have been normalized, meaning all values were divided by the maximum TTFF
value of all the trials in this test. Figure 4.3 shows the TTFF performance for 125 µs for
signal strengths from -130 dBm to -145 dBm.
From Figure 4.2, it can be concluded that up to -136 dBm, it is difficult to
distinguish the effect of different timing accuracies on AGPS performance. However, as
the signals get weaker, a trend can be seen. It is clear that a more accurate timing
accuracy leads to lower TTFFs. For example, at -142 dBm with 125 µs timing accuracy, a
30% improvement in TTFF can be expected over 250 µs and about a 45% improvement
over 500 µs.
65
Figure 4.2: Normalized TTFF for Different Timing Accuracies
Figure 4.3: Normalized TTFF for 125 µs Timing Accuracy
66
From Figure 4.3, which shows the TTFF performance with 125 µs timing
accuracy, it can be seen that the TTFFs increase significantly for signals lower
than -142 dBm. Once again, this may be due to the “acquisition maps” mentioned in
Section 3.3.1.
The position accuracy results at the three different timing accuracies are shown in
Figures 4.4 and 4.5 for -138 dBm and -140 dBm signal power. The 2D and 3D RMS
statistics are listed in Table 4.1. Table 4.2 presents the number of satellites acquired for
each of the scenarios listed in Table 4.1.
The 2D and 3D RMS statistics, which is used throughout the thesis, are computed
using the RMS values for latitude, longitude and height. The ‘2D RMS’ error is
calculated by squaring the RMS values for latitude and longitude and then taking the
square root. For the computation of the ‘3D RMS’ error, in addition to the RMS of
latitude and longitude, the RMS value of the height component is also used. These
statistics are not to be confused with Distance Root-Mean-Square (DRMS) error. This is
a measurement used to describe the accuracy of a fix. It is twice the square root of the
sum of the squares of all radial errors surrounding a true point divided by the total
number of measurements.
67
Figure 4.4: Position Errors for Different Timing Accuracies at -138 dBm
Figure 4.5: Position Errors for Different Timing Accuracies at -140 dBm
68
Table 4.1: 2D and 3D RMS Errors for Different Timing Accuracies
Power: -138 dBm 2D RMS Error (m) 3D RMS Error (m)
All
Fixes No First
Fix First Fix
Only All
Fixes No First
Fix First Fix
Only 125 µs 16.1 7.8 32.7 27.4 13.0 56.0 250 µs 14.0 10.6 23.8 20.6 17.2 31.5 500 µs 10.5 8.4 16.7 18.0 14.7 28.2 Power: -140 dBm 2D RMS Error (m) 3D RMS Error (m)
All
Fixes No First
Fix First Fix
Only All
Fixes No First
Fix First Fix
Only 125 µs 35.2 30.4 58.2 46.7 39.1 82.2 250 µs 24.2 16.7 52.1 32.3 24.7 63.6 500 µs 18.6 16.9 27.4 31.7 27.4 51.9
Table 4.2: Average Number of Satellites for Different Timing Accuracies
Power: -138 dBm No. of Satellites All Fixes No First Fix First Fix Only
125 µs 8.1 8.5 6.5 250 µs 7.3 7.5 6.5 500 µs 7.4 7.7 5.9
Power: -140 dBm No. of Satellites All Fixes No First Fix First Fix Only
125 µs 7.3 7.6 5.1 250 µs 7.2 7.5 7.0 500 µs 6.4 6.6 5.0
In Table 4.1, the first column includes all five position fixes from every trial
performed for that scenario. The column labelled ‘No First Fix’ means that for each trial,
the first position fix of that trial is excluded from the results presented in this column;
only four position fixes per trial are used to create the statistics for that column. Finally,
69
the column labelled ‘First Fix Only’ includes only the first position fix of each of the
trials.
The table is organized in this manner because the first position fix of each trial
seem to affect the results significantly and this can be seen clearly from the table. For
example, at -138 dBm with 125 µs timing, the 2D RMS error is 16.1 m. Without
including the first position fix of each trial, the 2D RMS error reduces significantly, down
to a value of 7.8 m. The 2D RMS error for only first fix is considerably higher, about
33 m.
At -138 dBm, the 2D RMS errors for 125 µs, 250 µs and 500 µs timing accuracies
are 16.1 m, 14.0 m and 10.5 m respectively while at -140 dBm, the 2D RMS errors are
35.2 m, 24.2 m and 18.6 m. These are the results with all of the position fixes included.
Similarly, the 3D RMS errors for the three different timing accuracies tested are 27.4 m,
20.6 m and 18.0 m at -138 dBm and at -142 dBm, the RMS errors are 46.7 m, 32.3 m and
31.7 m. There is a clear trend, the more accurate timing accuracies results in larger 2D
and 3D RMS errors. It is clear from Table 4.2 that the larger errors are not due to a lower
number of acquired satellites for the position fix. In fact, at both power levels, the
average number of satellites for all fixes was higher at a 125 µs timing accuracy than at a
250 or 500 µs time accuracy.
The statistics for the first fix (shown in columns labelled ‘First Fix Only’) clearly
show that for more accurate timing accuracies, the 2D and 3D RMS errors are much
larger. For example, at -138 dBm with 125 µs timing accuracy, the 2D RMS error for the
first fix is 32.7 m while the errors are 23.8 m and 16.7 m for 250 µs and 500 µs timing
70
accuracies respectively. A similar trend is seen for the timing accuracy tests conducted
with -140 dBm.
Without the first position fix (which is presented in the columns labelled ‘No First
Fix’), the trend discussed above does not hold. For example, at -138 dBm, the 125 µs
timing accuracy provided a lower 2D RMS error than both 250 µs and 500 µs timing
accuracy.
It can be concluded that there is a trade-off between the TTFF and position
accuracy. The more precise timing accuracies result in lower TTFFs but larger
positioning errors. These larger positioning errors are mainly due to the poor accuracy of
the first position fix.
4.2 Initial Position
In AGPS, it is assumed that the user receiver is close to the reference station and
consequently, an initial position for the user receiver can be approximated to be the same
location as the reference station. However, with the initial position, an uncertainty also
needs to be defined.
In the AGPS implementation under test, the uncertainty in the initial position is
taken into account with the use of two parameters: the Estimated Horizontal Error and the
Estimated Vertical Error. For example, the estimated horizontal error defines the radius
of uncertainty where the user might be located compared to the actual position of the
reference station. The receiver can use the initial position and uncertainties to determine
the satellites in view, reduce its search space and initialize the navigation solution
71
[Bryant, 2004]. In this section, the effect of the initial position will be investigated. The
effect of the position uncertainty will be investigated in Section 4.3.
A test was carried out to investigate the effect of the initial position on AGPS
acquisition performance. Simulator vehicle 1 was set at a fixed location with a power
level of -140 dBm. The location of simulator vehicle 2 (which is connected to the
reference receiver that supplies the initial position to the user AGPS receiver) was varied
such that the user-to-reference receiver distance was set to one of six values: 2 km, 10
km, 22 km, 30 km, 39 km or 55 km. The power level of vehicle 2 was set to -130 dBm.
The horizontal uncertainty was set to the same value as the user-to-reference
distance tested (i.e. if the reference was 22 km away from the user receiver, the horizontal
uncertainty was also set to 22 km; see Figure 4.6). The vertical uncertainty was set to
50 m for all cases. At least 30 trials were conducted for each of the distances. Here, a trial
is considered to be an acquisition from a cold start mode, followed by thirty position
fixes. The elevation mask of the AGPS receiver was set to 5° and the timing accuracy
provided by the TTB was 125 µs.
This scenario is intended to describe a real-life situation whereby the centre of the
cell sector can be taken as the initial position and the radius of the cell as the horizontal
uncertainty since the user can be anywhere within the specified radius of the cell sector.
Therefore, in urban areas, where there are many cell sectors, the horizontal uncertainty
would be smaller than in rural areas.
72
Figure 4.6: Scenario Representation for a User-to-Reference Distance of 22 km
The average TTFF for each initial position offset (i.e. user-to-reference distance)
tested is shown in Figure 4.7. The C3NAVG2 position domain results for two different
initial position offsets (2 km and 55 km) are shown in Figure 4.8 while the 2D RMS error
statistics for the tests are shown in Table 4.3.
Clearly, as the user-to-reference distance increases, the TTFF also increases. If the
user-to-reference distance was 55 km instead of 2 km, the average TTFF will be more
than twice compared to the 2 km situation. This result is significant, as it clearly indicates
that the AGPS TTFF performance will be better in urban areas than in rural areas,
assuming the cell coverage is better in an urban centre. This is clearly an advantageous
situation since most cellular phone users conduct their daily activities in an urban area.
One could also say that the rural users are less likely to need AGPS since they are more
likely to have a clear view of sky.
73
Figure 4.7: Average TTFF for Different User-to-Reference Distances
Figure 4.8: Position Errors for 2 km and 55 km Initial Position Offsets
74
Table 4.3: 2D RMS Errors for Different Initial Position Offsets
User-to-Reference Distance (km) 2D RMS Error (m)
2 19.2
11 24.1
22 32.0
30 24.3
39 18.5
55 36.2
No clear trend was observed in the position domain, with the position accuracy
between 18 – 36 m. The 2 km and 39 km position offset produced the best positioning
accuracy, approximately 19 m. The largest position offset, 55 km, produced the highest
errors, about 36 m. One might say that the position accuracy may also be better in an
urban area as compared to a rural area. However, keep in mind, that an urban area
presents many challenges including reduced satellite availability and multipath, which
will undoubtedly affect the position accuracy in a real-life scenario. The effect of
multipath on AGPS is not studied in this thesis.
A second simulation was created to evaluate the effect of the initial position offset
(i.e. user to reference distance) with a fixed horizontal uncertainty. This can be
considered to be a case where poor position assistance is provided to the AGPS receiver
75
from the reference receiver since the user to reference distance is much greater than the
horizontal uncertainty specified.
To investigate this scenario, the horizontal uncertainty was fixed to 2 km while
the user-to-reference distance was set to one of six values: 0 km, 11 km, 22 km, 30 km,
39 km and 55 km. As in the previous case, the power level of the vehicle connected to the
AGPS receiver was set to -140 dBm. At least 30 trials were conducted with thirty
position fixes in each trial. Once again, the elevation mask of the AGPS receiver was set
to 5° and the timing accuracy provided by the TTB was 125 µs.
Figure 4.9 shows the average normalized TTFF for the case where the horizontal
uncertainty was fixed to 2 km as well as the previous case where the horizontal
uncertainty matched the user-to-reference distance. Table 4.4 presents the 2D RMS errors
for the fixed horizontal uncertainty.
From Figure 4.9, it can be seen that up to a 22 km user-to-reference distance, the
TTFF for both the matching uncertainty and a 2 km fixed horizontal uncertainty is the
same. This result is surprising since the user was 22 km away from the reference receiver
yet the assistance provided indicated that it was at most 2 km away. If the AGPS receiver
was to accept the position assistance as correct, degradation in TTFF should have been
noticed and this was not the case up to 22 km. This may indicate that the AGPS receiver
under test have some protection built in for the case of poor position assistance. After all,
in real life, most cell sectors are less than 30 km, so it is not surprising that the AGPS
receiver may have algorithms to detect poor position assistance up to 30 km.
76
Figure 4.9: TTFF Comparison of Initial Position Offsets for Two Scenarios
However, as expected, when the user-to-reference is increased beyond 22 km, a
significant increase in TTFF was noticed when the horizontal uncertainty was fixed to
2 km. The AGPS receiver is still able to recover from the poor position assistance
provided but it takes significantly longer to acquire the signals. This is expected since the
fixed uncertainty of 2 km is an erroneous assistance.
77
Table 4.4: 2D RMS Errors for Different User to Reference Distances with a Fixed
Horizontal Uncertainty
User-to-Reference Distance (km) 2D RMS Error (m)
0 13.9
11 21.3
22 24.8
30 19.8
39 24.4
55 26.6
No clear trend can be seen with the positioning results. Most of the user-to-
reference distances tested resulted in 2D RMS errors around 20 – 25 m. It appears that
with a fixed horizontal uncertainty (2 km in this test), the user-to-reference distance has
no effect on the position accuracy.
4.3 Position Uncertainty
As mentioned in the previous section, the horizontal uncertainty defines the radius of
uncertainty where the user might be located compared to the actual position of the
reference station. This information, along with the initial position, can be used to reduce
the search space.
78
A test was carried out to investigate the effect of the varying horizontal
uncertainty on AGPS performance with a fixed user-to-reference offset. Only the effect
of the horizontal uncertainty was examined in this thesis.
Simulator vehicle 1 was placed at a fixed location. Similarly, simulator vehicle 2
(which is connected to the reference receiver) was placed at approximately 11 km away
from the user receiver (vehicle 1). This was purposely done to isolate the effect that an
exact initial position may have on receiver position calculation.
The power level of vehicle 1 was set to -140 dBm while the TTB was given
nominal signals (-130 dBm). The estimated horizontal error parameter (uncertainty) was
set to one of five values: 5 km, 10 km, 20 km, 50 km or 100 km. The estimated vertical
error was kept at 50 m for all cases. At least 30 trials were conducted at each horizontal
uncertainty (see Figure 4.10 for a representation of the scenario).
Another similar test was also performed, with a few modifications. The main
modification was that the initial user-to-reference distance was set to 30 km instead of the
11 km in the above test. In this test, the estimated horizontal error parameter was set to
following values: 5 km, 20 km, 30 km, 50 km and 100 km. Once again, the elevation
mask of the AGPS receiver was set to 5° and the timing accuracy provided by the TTB
was 125 µs.
79
Figure 4.10: Scenario Representation (11 km User-to-Reference Distance)
The average normalized TTFF for each horizontal uncertainty for both the 11 km
and 30 km user-to-reference offsets is shown in Figure 4.11. From the results, it can be
concluded that as the horizontal uncertainty remains within the user-to-reference
distance, there is no observable difference in TTFF. For example, for the 11 km user-to-
reference distance, all horizontal uncertainties below 11 km had similar TTFFs. The same
result was observed for the 30 km user-to-reference distance where uncertainties of 5 km,
20 km and 30 km produced similar TTFFs. However, when the horizontal uncertainty is
larger than the user-to-reference distance, the TTFF increases.
80
Figure 4.11: TTFF Performance for Different Horizontal Uncertainties
The position domain results for two different horizontal uncertainties (5 km and
50 km) for the 11 km user-to-reference distance are shown in Figure 4.12. The 2D RMS
error for the 11 km user to reference offset is shown in Table 4.5 while Table 4.6 shows
the 2D RMS error for the 30km user to reference offset. The position domain results were
processed using the C3NAVG software.
81
Figure 4.12: Position Errors for 5 km and 50 km Horizontal Uncertainties (11 km
User-to-Reference Distance)
Table 4.5: 2D RMS Errors for Different Horizontal Uncertainties (11 km User-to-
Reference Distance)
Horizontal Uncertainty (km) 2D RMS Error (m)
5 21.9
10 25.5
20 19.3
50 20.5
100 20.5
82
Table 4.6: 2D RMS Errors for Different Horizontal Uncertainties (30 km User-to-
Reference Distance)
Horizontal Uncertainty (km) 2D RMS Error (m)
5 26.5
20 33.1
30 19.7
50 26.9
100 35.9
No clear trend can be seen with the positioning results. In fact, all horizontal
uncertainties had similar results for the 11 km initial offset, with the 2D error around 20 –
25 m for all five uncertainties tested. For the 30 km initial offset, the 2D RMS error
varies from 20 m (for 30 km horizontal uncertainty) to 36 m for 100 km horizontal
uncertainty. It can be concluded that there is an effect on the TTFF domain but no impact
in the position domain when the horizontal uncertainty is varied.
4.4 Chapter Summary
In this chapter, the effect of aiding information was investigated. It was found that for
weak GPS signals (-140 dBm or lower), timing accuracy had a significant effect on
TTFF, with more accurate timing leading to lower TTFF values. However, there was a
trade-off between TTFF and position accuracy with more accurate timing leading to
larger positioning errors due to the inaccuracy of the first fix. Previous field tests
83
performed with the SiRFLoc receiver showed that the first position fix always had a
larger error than the subsequent fixes [Garin et al., 2002].
In terms of initial position, as the user to reference distance increases, the TTFF
also increases. No clear trend was observed with the position accuracy. In terms of
position uncertainty, it was found that if the horizontal uncertainty was within the user-to-
reference distance, the TTFF remains unchanged. For a horizontal uncertainty larger than
the user-to-reference distance, the TTFF increases. No clear trend was observed in the
position domain. The AGPS receiver under test is able to recover from poor/erroneous
position assistance but as expected, it takes significantly longer to acquire the signals.
84
CHAPTER 5: RF Interference on AGPS
In this chapter, the effect of various RFI on AGPS acquisition and tracking will be
investigated. Continuous Wave, Amplitude Modulation and Frequency Modulation
in-band interference will be studied.
5.1 Sources of RF Interference
As mentioned in Section 2.3.4, RFI is a major source for degradation of the GPS
accuracy and reliability. Since there are other sources of errors which further degrade
GPS accuracy, this makes RFI mitigation more difficult. Satellite and user motion
introduce Doppler effects, slow power fluctuations (due to changes in the effective
antenna gain and path loss) and fast power changes (due to multipath fading, blockage
and shadowing) [Heppe and Ward, 2003]. Doppler fluctuations make it difficult to
distinguish between user motion and receiver clock drift. Power fluctuations make it
difficult to determine the thresholds for acquisition and tracking while atmospheric errors
introduce range and range-rate errors.
The signals, or the harmonics of the signals, near the GPS frequencies (L1 and
L2), are potential sources of interference. Interference can also be caused by ionospheric
scintillation and evil waveforms transmitted by the GPS satellites themselves [Geyer and
Frazier, 1999]. Unintentional interference can be caused by RF transmitters, harmonics of
ground transmitters, radar signals and accidental transmission of signals in the wrong
frequency band [Spilker and Parkinson, 1996]. Pulsed interference can result from radar
85
signals in nearby frequency bands which are not properly filtered [Littlepage, 1999].
Table 5.1 summarizes various types of RFI.
Table 5.1: Types of RFI and possible sources [Kaplan, 1996]
Type Typical source
Wideband-Gaussian Intentional noise jammers
Wideband phase/frequency modulation Television transmitter’s harmonics or near-band microwave link transmitters
Wideband-spread spectrum Intentional spread spectrum jammers or near-field of pseudolites
Wideband pulse Radar transmitters
Narrowband phase/frequency modulation AM stations transmitter’s harmonics
Narrowband swept continuous wave Intentional CW jammers or FM stations transmitter’s harmonics
Narrowband continuous wave Intentional CW jammers or near-band unmodulated transmitter’s carriers
CW interference can be either a pure tone or a narrow band modulated signal such
as AM or FM [Macabiau et al., 2001]. It adds to the signal spectrum and can affect the
carrier tracking. A carrier tracking loop will lock onto the interference frequency for a
pure tone signal generating erroneous carrier phase and Doppler measurements (provided
the CW power level is considerably high). Broadband noise increases the amount of noise
in the GPS spectrum without distorting the signal spectrum [Heppe and Ward, 2003].
Swept CW interference is more damaging than CW interference because it can cover
multiple Doppler frequencies and affect more than one receiver channel at the same time.
Pulse interference can cause malfunctioning of the AGC which affects the tracking loops
[Hegarty et al., 2000].
86
Mobile phones use FM signals for communication and the incorporation of GPS
into a cellular handset means that a jammer will be operating nearby at the cellular
frequency. For example, GSM phones used in Europe work either on the 900 MHz or
1800 MHz frequency bands while North American GSM phones primarily use the 1900
MHz band. CDMA technology is the basis for Interim Standard 95 (IS-95) and operates
in both the 800-MHz and 1900-MHz frequency bands in the US. The major US carriers
using CDMA are Air Touch, Bell Atlantic/Nynex, GTE and Primeco. In 1994, the FCC
announced it was allocating spectrum specifically for Personal Communication Services
(PCS) technologies at the 1900 MHz band [About.com, 2005]. A summary of handset
frequencies and power levels that will manifest themselves as out-of-band GPS jammers
is provided in Table 5.2.
Table 5.2: Mobile Frequencies and Power Levels [from Paddan et al., 2003]
Cellular Standard Transmit Freq (MHz) Max. Handset Output Power
GSM 880-915 and 1710-1785 +33 dBm
IS-95 824-849 +23 dBm PCS 1850-1910 +24 dBm
A high jammer power level can cause the generation of unwanted mixing
products (spurs) if the level exceeds the linear range of the circuit blocks. An out-of-band
jammer mixes with spectral components to create spurs in the same frequency band as the
desired signal. If the power levels are high enough, the resulting spurs may possibly
exceed the linear range of the circuit, resulting in the receiver's inability to retain GPS
87
signal lock. There is a possibility of these signals interfering with GPS signals and
causing problems for acquisition. Therefore, care must be taken to isolate the mobile
signals from GPS to avoid interference and jamming of the GPS signals [Deshpande,
2004].
AGPS-capable cell phones will be subjected to RFI from other sources which can
cause degradation of GPS accuracy and reliability. Higher order harmonics of AM and
FM radiobroadcast transmitters emissions fall close to the GPS L1 frequency (1575.42
MHz), which can potentially cause interference. With an AM broadcast, the harmonic
order is very high (985) and the likelihood of RFI is minimal. However, for an FM
broadcast, the harmonic order is lower (15 to 18) and the maximum effective isotropic
radiated power (EIRP) is higher (50 to 60 dBW). Analog TV broadcast maximum EIRP
limits are higher than for FM, while harmonic orders are lower (2 to 9 for RFI signals
within 2 MHz of GPS L1) and will cause more interference [Erlandson and Frazier,
2002].
Buck and Sellick [1997] analyzed the effects of the harmonics of the TV signals
interfering with GPS frequencies and they were found to be in the L1 signal spectrum
causing a non-linear effect. The strongest suspected interference signal was at
525.25 MHz which is the video carrier of a local UHF TV station (Channel 23). Thus the
1575.75 MHz signal was the third harmonic of the local station video carrier. The GPS
L1 frequency divided by three is 525.14 MHz and the transmitted TV signal's lower side
band suppression was at 524.50 MHz thereby allowing full power at this frequency. This
jump in power will produce a high level of interference resulting in a reduced SNR.
88
Filters were found to be effective in eliminating these interference signals by having high
attenuation for the undesired signals. The TV and Air Traffic Control (ATC) frequencies
have high transmitter powers and their harmonics fall in the GPS L1 frequency band. The
best protection for a GPS receiver is to use RF filtering to exclude the unwanted
interference. Spurious transmissions from RF transmitters in the GPS frequency band
should be measured to allow its suppression [Johannessen et al., 1990].
Previously, research has been done on the effect of interference on GPS
performance. Betz [2000] developed expressions to describe the effect of narrowband
interference on code tracking accuracy and C/N0 ratio, which shows that interference at a
frequency mid-way between the carrier and the first null has the greatest overall effect.
The expressions depend on the early-late spacing, the integration time, the unjammed
carrier-to-noise density ratio, and the tracking loop’s equivalent rectangular bandwidth.
Deshpande [2004] analyzed the interference effects on the acquisition process. RF
interference distorts the autocorrelation peak and leads to a false acquisition. However,
the power required to prevent or jam the acquisition process largely depends on the types
of interference. A relative CWI power of 15 dB is needed to jam this process while a
relative FM power of 35 dB is needed.
Other research was performed by Johnston [1999], Burns et al. [2002], and
Deshpande and Cannon [2004]. Most of that research was performed using a software
receiver and focused on acquisition. However, little research has been conducted to
investigate the effect of interference on AGPS receiver acquisition and tracking
performance.
89
5.2 Interference Effects
RFI has the same effect on GPS acquisition or tracking as signal blockage, foliage
attenuation, ionospheric scintillation and multipath, which is to reduce the C/N0 for all
the GPS signals. A jammer reduces the SNR of the GPS signals affecting acquisition and
tracking of the signals in the GPS receiver. Spoofing is another form of interference
which transmits a stronger version of the GPS signal to capture the receiver loops and
fool the receiver [Heppe and Ward, 2003]. Pseudolites operating at close range to a
receiver can jam the GPS receiver. The primary aspect of the GPS architecture that
makes it vulnerable is the low power of the signal which is actually below the noise floor
until it is de-spread with an appropriate PRN code. The RFI effect depends on the details
of the receiver design, especially the front-end bandwidth and early-late spacing in the
discriminator [Macabiau et al., 2001]. It has a different effect on the code tracking
accuracy than it does on some other aspects of the GPS receiver [Geyer and Frazier,
1999]. Several types of perturbations like thermal noise, atmospheric disturbances,
multipath and interference can affect the GPS signal. Geyer and Frazier [1999] conducted
tests on a C/A-code receiver for the Federal Aviation Administration (FAA) to determine
the vulnerability of the GPS receivers to RFI. This allowed the FAA to establish
interference standards for GPS receivers used in civil aviation. These tests were focused
on the C/A-code receiver’s tracking degradation and loss of lock under different
interference conditions. The GPS signal was found to be vulnerable to very high
frequency (VHF) transmissions and CW interference.
90
RFI detection should be given high priority because it provides an instantaneous
warning of the potential loss of GPS integrity. It can be detected using a jamming-to-
noise (J/N) power ratio meter [Kaplan, 1996]. The J/N meter is implemented in the AGC
of the GPS receiver front-end. This meter keeps a check on the thermal noise level and
any signal different from it, is detected as the presence of an interference signal.
The C/N0 for a satellite vehicle (SV) signal without interference is termed as
unjammed C/N0 [ibid]. The difference between the unjammed C/N0 and the acquisition or
the tracking threshold gives an indication of the possible interference tolerance and is
termed as effective C/N0. The unjammed C/N0 and the effective C/N0 are used to
compute the maximum jammer-to-signal (J/S) level at the receiver input from which the
RFI power can be determined. The unjammed C/N0 depends upon the GPS receiver
parameters and is computed as follows [Kaplan, 1996]:
Hz)-(dB L - Nf - (kTo) 10log- Ga Sr / 0 +=NC (5.1)
where
Sr is the received GPS signal power (dBW),
Ga is the antenna gain towards the SV (dBic),
10log(kTo) is the thermal noise density (dB-Hz) ≅ -204 dBW-Hz,
k is the Boltzmann’s constant (watt-sec/K) = 1.30 x 10-23,
To is the thermal noise reference temperature (K) = 290 K,
Nf is the noise figure of the receiver (dB), and
L is the implementation loss plus ADC loss (dB).
91
Signal information is lost during conversion of the signal from analog to digital
by the ADC which is referred to as the ADC loss. The level to which the unjammed C/N0
is reduced by the RFI is called the equivalent C/N0 power density ratio. The equivalent
C/N0 power density ratio is related to unjammed C/N0 and J/S as given by the following
equation [Kaplan, 1996]:
Ratio)Power ( -1(J/S)/QR)) -1((C/No) eq]/[ 0 +=NC (5.2)
where
C/N0 is the unjammed carrier-to-noise power in a 1 Hz bandwidth
expressed as a ratio,
J/S is the jammer-to-signal power expressed as a ratio,
R is the GPS PRN code chipping rate (chips/sec), which is 1.023x106
chips for the C/A code and 10.23x106 chips for the P code, and
Q is the spread spectrum processing gain adjustment factor, and is 1
for narrow band jammer, 1.5 for wide spread spectrum jammer and
2 for wideband Gaussian noise jammer.
Equation (5.2) can be expressed in terms of dB-Hz, which is shown in Equation
(5.3). This equation can be rearranged to obtain J/S [Kaplan, 1996]:
Hz)(dB]/10/)/(1010/)/(10[log10]/[ 0 −+−−= QRSJNoCeqNC (5.3)
(dB) ]10 - (10 [QR log 10 J/S ) -(C/No)/10)/10-([C/No]eq= (5.4)
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For a C/A-code receiver, with a received signal strength, Sr = -159.6 dBW and
assuming the antenna has unity gain toward the SV (Ga = 0), a noise figure of 4 dB and
an implementation loss of 2 dB, then the unjammed C/N0 is 38.4 dB-Hz. For Q=2, and
assuming an equivalent C/N0 threshold of 28 dB-Hz, the J/S = 34.7 dB [Kaplan, 1996].
This tolerance looks good in terms of dB but when converted to the actual signal power,
it is just 3 pW. The RF transmitter transmits signals with high power levels (in terms of
Watts) and hence the harmonics of these signals can have power levels greater than 3
pW. This will result in jamming of the GPS receiver and hence RFI detection and
mitigation is important in a GPS receiver.
A number of techniques have been designed to increase the robustness of a GPS
receiver to RFI signals [Littlepage, 1999]. RFI can be mitigated at various stages of the
GPS receiver from the instant of receiving the GPS signals by the antenna to the position
computation stage. RFI signals will have full effect when the interference signal is
unobstructed and the antenna provides adequate gain to the signal.
5.3 Interference Tests
In the following sections, the effect of CW, AM and FM interference on AGPS
acquisition and tracking performance is investigated. The Agilent signal generator
(E4431B) was used to generate the various interference signals. The GPS signals
generated using GSS6560 and the interference signals are combined using an interference
combiner GSS4766. The interference combiner introduces a loss while combining the
input signals. This loss was measured and found to be around 8-10 dB for each channel.
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This was taken into account to obtain the desired signal strength at the output of the
interference combiner.
5.3.1 CW Interference
Continuous wave interference was tested in an in-band range of the GPS L1 frequency.
Figure 5.1 shows the test setup used to conduct interference tests for various types of
RFI. All simulation tests in the interference section were designed with 10 to 12 satellites
in simulation with no orbital, atmospheric or any other errors. Only the effect of noise on
receiver performance under various types of RFI will be seen.
Figure 5.1: Interference Test Setup
A timing accuracy of 125 µs was provided to the AGPS receiver. The horizontal
uncertainty was set to 30 km while the vertical uncertainty was set to 50 m. The values
used for timing accuracy and the position uncertainties represent typical values used in an
AGPS implementation. Data on the AGPS receiver was collected at least 90 seconds after
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the TTB was initialized to ensure that the TTB was able to obtain a position fix with at
least seven satellites. The TTB was given nominal signals, -130 dBm.
The GPS L1 frequency was used as the carrier frequency for the CW signals
analyzed. The interference power levels were varied from 0 to +50 dB relative to the GPS
signal power, which was set to -130 dBm. It should be noted that only the three SiRF
receivers were used for the acquisition tests while the OEM4 was used only for the
tracking tests. The elevation mask was set to 5°.
Table 5.3 shows the amount of CW interference power (relative to -130 dBm) that
each of the receivers is able to tolerate in acquisition and tracking. In acquisition, the
AGPS was able to tolerate 30 dB relative CW interference power, meaning it was able to
tolerate up to -100 dBm of CW interference power. Both the HSGPS and standard
receivers were able to withstand -110 dBm of CW interference power. AGPS was able to
tolerate 10 dB more CW interference than HSGPS and standard receivers. Better
performance from AGPS is expected since the acquisition sensitivity of the AGPS
receiver is higher than the HSGPS receiver.
Table 5.3: Acquisition and Tracking Threshold under CW Interference
Receiver Acquisition (dB) Tracking (dB)
AGPS 30 40
HSGPS 20 40
Standard 20 25
OEM4 N/A 40
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In tracking, the AGPS and HSGPS receivers were able to track up to 40 dB
relative CW interference power. It is not surprising that the AGPS performance in
tracking is similar to the HSGPS since aiding provides “coarse” estimates intended to
assist acquisition. This “coarse” assistance is not useful in improving tracking
performance because when the receiver is in tracking mode, it has a much more precise
GPS time and location.
The Standard SiRF receiver was only able to track with 25 dB relative CW
interference power. However, the conventional geodetic OEM4 receiver was able to track
up to 40 dB, the same level as the AGPS and HSGPS receivers. In all cases, the tracking
threshold was at least 5 dB higher than the acquisition threshold. In the case of the
HSGPS receiver under CW interference, a 20 dB improvement in tracking over
acquisition was seen.
The 2D errors as a function of relative CW interference power for acquisition and
tracking tests are shown in Figure 5.2 and Figure 5.3. The 2D RMS error for different
interference power levels during acquisition and tracking is shown in Figure 5.4.
It should be noted that the position results were obtained by C3NAVG2TM post-
processing with a Horizontal DOP limit of 5 and Vertical DOP limit of 5. Dilution of
Precision (DOP) is a dimensionless number that accounts for the contribution of relative
satellite geometry to errors in the position determination. DOP has a multiplicative effect
on the user equivalent range error (UERE). All of the position results in the interference
section are obtained using the above criteria.
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Figure 5.2: 2D Error vs. Relative CW Interference Power (Acquisition)
Figure 5.3: 2D Error vs. Relative CW Interference Power (Tracking)
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Figure 5.4: 2D RMS Error for each CW Interference Power Interval
During acquisition, all of the receivers were able to give a position within less
than 4 m (RMS) until 20 dB of interference power. The AGPS, which had the most
tolerance under CW interference, was able to acquire up to 30 dB but the position
accuracy degrades at the upper limits to approximately 8 m. This shows a trade-off
between interference tolerance and position accuracy. In addition, the acquisition success
at 30 dB for the AGPS receiver was approximately 25%.
In tracking mode, the OEM4 receiver provided the best position accuracy. This is
consistent with previous results; research performed by Kim [2005] has also found that
the OEM4 provided better position results than the SiRFXTrac receiver. Once again, at
the upper limits of each receiver, severe performance degradation was seen. In the case of
the AGPS receiver, the 2D RMS error was greater than 30 m. At 40 dB, more than 25%
of the errors had a 2D RMS error of over 30 m.
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5.3.2 AM Interference
An AM signal is a CW signal whose amplitude varies as a function of the modulating
signal. AM is used widely for radio communications. The AM signal was tested with the
carrier frequency at the GPS L1 frequency and the modulating signal was set to 10 Hz
while the modulation depth of the AM signal was set to 50%. The modulation depth
determines the amount of modulation present in the signal with higher modulation depths
resulting in more noise [Deshpande, 2004].
The interference power levels were varied from 0 to +50 dB relative to the GPS
signal power, which was set to -130 dBm. Table 5.4 shows the acquisition and tracking
thresholds of each of the receivers used.
Table 5.4: Acquisition and Tracking Threshold under AM Interference
Receiver Acquisition (dB) Tracking (dB)
AGPS 30 40
HSGPS 25 40
Standard 20 25
OEM4 N/A 40 In acquisition, the AGPS was able to tolerate 30 dB relative AM interference
power, meaning it was able to tolerate up to -100 dBm of AM interference power. The
HSGPS receiver was able to withstand -105 dBm of AM interference power while the
standard receiver was able to acquire up to -110 dBm. The AGPS receiver was able to
tolerate 5 dB more AM interference than an HSGPS receiver and 10 dB more than a
conventional receiver. As in the case with CW interference, three of the receivers
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(AGPS, HSGPS and geodetic conventional receivers) are able to track up to 40 dB
relative AM interference power.
The SiRF Standard tracking performance was comparably lower; it was only able
to tolerate 25 dB of AM interference. The better performance from the OEM4 compared
to the SiRF Standard, even though they are both conventional GPS receivers with similar
tracking performance in normal GPS conditions, is due to the fact that the OEM4 uses
narrow chip spacing and its tracking loops have a lower bandwidth. In all cases, the
tracking threshold was higher than acquisition threshold.
The 2D errors as a function of relative AM interference power while acquiring
and tracking are shown in Figure 5.5 and Figure 5.6. The 2D RMS error for different
interference power levels during acquisition and tracking is shown in Figure 5.7.
From Figure 5.6, one can clearly see that at the upper limits of each receiver, the
position accuracy degrades significantly. For the SiRF Standard receiver, at its tracking
threshold of 25 dB of AM interference power, the 2D errors exceed 50 m.
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Figure 5.5: 2D Error vs. Relative AM Interference Power (Acquisition)
Figure 5.6: 2D Error vs. Relative AM Interference Power (Tracking)
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Figure 5.7: 2D RMS Error for each AM Interference Power Interval
During acquisition under AM interference, all of the receivers were able to give a
position within less than 5 metres (RMS) up to 20 dB of AM interference power. As in
the case of CW interference, the AGPS receiver was able to acquire up to 30 dB relative
interference power but the position accuracy degraded at the upper limits, close to 25 m
of 2D RMS error. At 30 dB for the AGPS receiver, there is a large error of over 180 m
which resulted in a biased 2D RMS error. The HDOP at this epoch was 1.85 and four
satellites were used to compute the position. Removing that one large error, the 2D RMS
error becomes 8.1 m, which is similar to the acquisition results with CW interference.
In the case of tracking with the AGPS receiver, the 2D RMS error at 35 dB
appears to be greater than that of 40 dB. This is due to four epochs that affected the
results. When these four epochs are removed from the solution, the 2D RMS error for
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AGPS at 35 dB is about 2.75 m, which is much lower than the 2D RMS error at 40 dB.
Significant position accuracy degradation is noticed with the SiRF Standard receiver at
25 dB. There were many large errors; more than 15% of the errors at 25 dB were over 30
m.
5.3.3 FM Interference
An FM signal is a continuous wave signal whose frequency varies as a function of the
modulating signal. FM signals are used for radio broadcasts in the 88-108 MHz
frequency range, audio in television and for cellular transmission at various frequencies
listed in Table 5.2. The signal level of these FM signals is very high and the high order
harmonics of FM signals in the GPS frequency band will have considerable power as
compared to GPS signal levels. This will result in interference from the FM signal that
needs to be mitigated.
The GPS L1 frequency was used as the carrier frequency for the FM signals
analyzed. The modulating frequency was kept at 10 Hz while the frequency deviation
was set to 1 MHz. The modulation frequency in the FM signal decides the rate at which
the frequency deviates from the centre frequency. A smaller modulation frequency has
less frequency variation and the interference signal appears like a CW frequency
[Deshpande, 2004]. The interference power levels were varied from 0 to +50 dB relative
to the GPS signal power, which was set to -130 dBm.
The acquisition and tracking threshold of each of the receivers used is shown in
Table 5.5. In acquisition, the AGPS was able to tolerate 10 dB more FM interference than
an HSGPS receiver and 15 dB more than a conventional receiver. In tracking, AGPS and
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HSGPS receivers were able to tolerate up to a 40 dB relative AM interference power, the
same level as for CW and AM interference. The Standard receiver was only able to
tolerate up to 30 dB relative power while the OEM4 was able to tolerate up to 35 dB of
relative FM interference power. Once again, in all cases, the tracking threshold was
higher than the acquisition threshold.
Table 5.5: Acquisition and Tracking Threshold under FM Interference
Receiver Acquisition (dB) Tracking (dB)
AGPS 35 40
HSGPS 25 40
Standard 20 30
OEM4 N/A 35 As mentioned previously, C/N0 is the best measurable value of the signal quality
present at the input to a GPS receiver. It is an instantaneous measure of the ratio of the
carrier power present to noise power density measured per Hertz of bandwidth. Figure
5.8 and Figure 5.9 show the average C/N0 for all satellites and the relative FM
interference power during acquisition and tracking tests. In acquisition, all of the
receivers are able to tolerate up to 20 dB of relative interference power before the C/N0
decreased.
The 2D error as a function of relative FM interference power is shown in Figure
5.10 and Figure 5.11. The 2D RMS error for different interference power levels during
acquisition and tracking is shown in Figure 5.12.
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Figure 5.8: Average C/N0 vs. the Relative FM Interference Power (Acquisition)
Figure 5.9: Average C/N0 vs. the Relative FM Interference Power (Tracking)
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Figure 5.10: 2D Error vs. Relative FM Interference Power (Acquisition)
Figure 5.11: 2D Error vs. Relative FM Interference Power (Tracking)
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Figure 5.12: 2D RMS Error for each FM Interference Power Interval
During acquisition under FM interference, all of the receivers were able to give a
position within less than 5 m (RMS) up to 20 dB of FM interference power. Once again,
the position accuracy degraded at the upper limits, similar to the results seen with CW
and AM interference. For example, the AGPS receiver was able to provide position
within less than 9 metres at 35 dB relative FM interference power.
In tracking mode, the position accuracy with FM interference was much better
than under CW or AM interference. All of the receivers provided position within an
accuracy of less than 5 m (RMS), even at the upper limits. The OEM4 receiver provided
the best position accuracy in tracking under FM interference.
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5.4 Chapter Summary
In this chapter, the effect of CW, AM and FM in-band interference (close to the GPS L1
frequency) on receiver acquisition and tracking performance was analyzed. It was found
that the AGPS receiver was able to tolerate 5 to 10 dB more interference power than an
HS receiver and 10 to 15 dB more than a conventional receiver in acquisition mode. Both
AGPS and HS had similar performance while tracking. In all RFI cases, all the receivers
were able to tolerate more interference while tracking than in acquisition. In tracking, the
OEM4 receiver provided the best positioning accuracy under all RFI cases.
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CHAPTER 6: User Dynamics on AGPS
In this chapter, the effect of user dynamics on AGPS acquisition and tracking will be
investigated. User dynamics increase the frequency uncertainty by about 1.46 Hz per
kilometre per hour [van Diggelen, 2001a]. As a result, user movement increases the
Doppler search range (by as much as ±300 Hz for high dynamic situations) in the
acquisition process and consequently, the TTFF should also increase. They also limit the
duration of the pre-detection integration time since the Doppler varies quickly for high
user dynamics. Aiding data provided by the cellular network server helps to reduce the
search space and may improve the acquisition performance under dynamic conditions of
an AGPS receiver compared to other GPS technologies such as HSGPS receivers.
6.1 Effect of Velocity
6.1.1 Acquisition
An acquisition test was performed to investigate the effect of different velocities
on receiver acquisition performance. For this test, the power level of each satellite in
simulator vehicles 1 and 2 was kept at -130 dBm. As shown in Figure 6.1 which
illustrates the test setup, simulator vehicle 2 was connected to the TTB (which is the
reference receiver that provides assistance to the AGPS receiver) and this vehicle was
stationary throughout the test.
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Figure 6.1: Dynamics Test Setup
A 20-km trajectory as shown in Figure 6.2 was created. Initially, the vehicle
accelerated to a prescribed velocity over a 15 second interval and then maintained this
velocity. At this velocity, the simulated car followed the 20-km trajectory for the
remainder of the test. The acquisition test was started after the prescribed velocity was
reached. The velocity of vehicle 1 was set to one of five levels: 36 km/h, 72 km/h,
108 km/h, 180 km/h and 360 km/h. These dynamic scenarios represent different
velocities that may be experienced while driving. The 360 km/h is used to test the
sensitivity of the receiver as this would require a really fast automobile. The
specifications state that the AGPS receiver is able to operate up to 300 km/h while the
limit for the SiRFXTrac is listed as approximately 1850 km/h (515 m/s). It is worth
noting that the specifications are quite different for the two receivers despite being
designed from the SiRFstarII architecture.
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Figure 6.2: Dynamics Test Trajectory
For the AGPS receiver, the precise timing accuracy provided by the TTB was set
to 125 µs. The horizontal uncertainty was set to 30 km. This uncertainty is suitable since
the entire trajectory is only 20 km. The vertical uncertainty was only set to 50 m for all of
the velocities tested since the height was not varied during the test (i.e. the test trajectory
was purely horizontal). The position of the simulated automobile was taken as the truth
trajectory.
At each velocity, at least 30 trials were conducted. For the dynamic test, a trial is
considered to be an acquisition from a cold start followed by 60 position fixes to give
sufficient samples for analysis. The receiver is then cold started to start a new trial. It is
assumed that with a cold start, ephemeris, almanac and an initial position is not known
prior to acquiring the GPS signal. The raw pseudorange data was extracted at 1 Hz from
each receiver and post-processed using the C3NAVG2TM software.
Figure 6.3 shows the TTFF values for different velocities for both the AGPS and
HSGPS receivers. All TTFF values have been normalized, meaning all values were
divided by the maximum TTFF value of all the trials in this particular test. The TTFF is
at least four times lower with the AGPS receiver over the HSGPS receiver. With the
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HSGPS receiver, an increase in TTFF is seen as the velocity increases but the TTFF for
the AGPS remains constant. As expected, the aiding data provided to the AGPS receiver
helps to reduce the search space in acquisition and consequently, a TTFF improvement is
seen with AGPS over the HSGPS. The 2D position errors as a function of velocity for
both receivers are shown in Figure 6.4 and Figure 6.5.
Figure 6.3: Average TTFFs for Different Velocities
From Figure 6.4 and Figure 6.5, it is clear that as the velocity increases, the
position accuracy decreases. The AGPS and HSGPS receivers have similar performance.
In Figure 6.6, the AGPS position errors (latitude, longitude and height) for different
velocities are shown. From this figure, it is clear that the longitude errors are larger for
360 km/h compared to 180 km/h test. The height errors remain relatively the same since
the test trajectory was purely horizontal.
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Figure 6.4: 2D Position Error for AGPS (Acquisition)
Figure 6.5: 2D Position Error for HSGPS (Acquisition)
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Figure 6.6: AGPS Position Errors for 72 km/h, 180 km/h and 360 km/h
The velocity errors as a function of velocity for the AGPS receiver are shown in
Figure 6.7. It is clear that as the velocity increases, the velocity errors also increase.
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Figure 6.7: AGPS Velocity Errors for Acquisition Test
6.1.2 Tracking
The above results indicate the effect of user dynamics on AGPS acquisition performance.
In order to determine the effect on tracking performance, another test was conducted. The
setup and procedure was similar to the one described above with the only exception being
that the receiver was not cold started between trials. Figure 6.8 shows the 2D position
error for AGPS and HSGPS receivers while tracking under different velocities.
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Figure 6.8: 2D Position Error for AGPS and HSGPS (Tracking)
It is clear that as the velocity increases, the position accuracy decreases. This
trend is observed clearly with the AGPS receiver. However, with the HS receiver, the 2D
error results for 100 km/h do not seem to fit the general trend. This is due to a higher
latitude RMS for this case compared to all other tests. If 95% of the best data are used,
the increasing trend holds for all values. Overall, one can conclude that the 2D error
performance is similar for both AGPS and HSGPS receivers.
Figure 6.9 shows the velocity errors for the AGPS receiver while tracking under
different velocities. It is clear that as the velocity increases, the velocity errors also
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increase. During tracking, the velocity errors appear to be less than the errors seen during
the acquisition test. For example, at 180 km/h, the velocity errors are 12 km/h during
acquisition while they are approximately 7.5 km/h while tracking.
Figure 6.9: AGPS Velocity Errors for Tracking Test
6.2 Effect of Acceleration
6.2.1 Acquisition
Another test was conducted to investigate the effect of acceleration on AGPS acquisition
performance. The test setup was similar to the one used in the velocity section. Once
again, the power level of each satellite in simulator vehicles 1 and 2 was kept
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at -130 dBm. The acceleration of vehicle 1 was set to one of four levels: 0.5 m/s2, 1 m/s2,
2 m/s2, and 4 m/s2. These values were chosen to represent moderate accelerations that
may be experienced while driving a vehicle. For example, the acceleration of a vehicle
that goes from 0 to 100 km/h in 8.0 seconds is approximately 3 m/s2.
Figure 6.10 shows the average TTFF under different accelerations. The position
errors for the AGPS receiver for two accelerations (0.5 m/s2 and 4 m/s2) are shown in
Figure 6.11 while 2D RMS errors are shown in Table 6.1.
Figure 6.10: Average TTFFs for Different Accelerations
As in the case with varying velocity, the TTFF remains the same for the AGPS
receiver under different accelerations. For the HSGPS receiver, an increase in TTFF is
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seen as the acceleration increases. It should be noted that one large TTFF (more than
twice the average) biased the 1.0 m/s2 HSGPS result. As expected, the aiding data
provided to the AGPS receiver helps to reduce the search space in acquisition and
consequently, a TTFF improvement is seen with AGPS over the HSGPS.
Figure 6.11: AGPS Position Errors for 0.5 m/s2 and 4 m/s2 (Acquisition)
From Figure 6.11, it can be seen that for the AGPS receiver, the errors increase as
the acceleration increases. Position errors larger than 50 m are indicated on the 50 m line,
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at the top of the figure with red, green and blue dots. Clearly, there are many more errors
over 50 m for the 4 m/s2 case as compared to the 0.5 m/s2 case.
Table 6.1: 2D RMS Errors for Different Accelerations (Acquisition Test)
AGPS HSGPS Acceleration (m/s2)
2D RMS Error (m) 2D RMS Error (m) 0.5 16.1 19.1 1.0 26.9 34.0 2.0 75.3 74.6 4.0 359.3 606.1
As acceleration increases, the 2D RMS errors increase for both AGPS and
HSGPS receivers. In the case where the acceleration is 4.0 m/s2, the 2D RMS error for
both receivers is very large. This is due to the fact that at this acceleration, there were
many errors that were over 2000 m that biased the results. When large errors occurred,
only four satellites were used to compute the position and in most cases, two or three
satellites were rejected by the post-processing software due to range residual checking. If
95% of the best data is chosen for the 4.0 m/s2 case, the resulting 2D RMS error is 59.6 m
for AGPS and 70.8 m for HSGPS. For most of the accelerations tested, the position
accuracies for both AGPS and HSGPS receivers are similar.
6.2.2 Tracking
A test was conducted to investigate the effect of acceleration on AGPS tracking
performance. The test setup was similar to the one used in the acquisition section except
no cold starts were performed during the trial. Once again, the acceleration of vehicle 1
was set to one of four levels: 0.5 m/s2, 1 m/s2, 2 m/s2, and 4 m/s2.
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The position errors for the AGPS receiver for two accelerations (0.5 m/s2 and
4 m/s2) are shown in Figure 6.12. The 2D RMS errors for both AGPS and HSGPS
receivers are shown in Table 6.2.
Figure 6.12: AGPS Position Errors for 0.5 m/s2 and 4 m/s2 (Tracking)
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Table 6.2: 2D RMS Errors for Different Accelerations (Tracking Test)
AGPS HSGPS Acceleration (m/s2)
2D RMS Error (m) 2D RMS Error (m) 0.5 14.8 35.0 1.0 23.1 25.2 2.0 82.9 149.2 4.0 151.6 190.7
From Figure 6.12, it can be seen that for the AGPS receiver, the errors increase as
the acceleration increases. This was also the conclusion that was reached for the velocity
scenario in Section 6.1.2. There are many more errors over 50 m for the 4 m/s2 case as
compared to the 0.5 m/s2 case.
A trend similar to the performance of the AGPS under constant acceleration can
be seen for the HSGPS receiver as well. However, for the HSGPS receiver, a larger 2D
RMS error is seen for the 0.5 m/s2 as compared to 1.0 m/s2. If 95% of the best data is
chosen for the HSGPS receiver, the resulting 2D RMS error is 2.3 m for 0.5 m/s2 and
7.9 m for 1.0 m/s2.
6.3 Chapter Summary
In this chapter, the effect of user dynamics on AGPS acquisition and tracking was
investigated. It was found that for an AGPS receiver, increasing velocity had no effect on
TTFF performance but the position accuracy decreased. With an HSGPS, the TTFF
increased as the velocity increased and the position accuracy decreased.
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When tested with different accelerations, the TTFF remained the same for an
AGPS receiver while it increased for an HSGPS receiver. In terms of position, the
accuracy of the position decreased for both AGPS and HSGPS receivers with increasing
acceleration, whether in acquisition or tracking mode.
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CHAPTER 7: Conclusions and Recommendations
The FCC E-911 mandate, Location-Based Services, as well as personal and vehicular
navigation applications are driving the need for navigation capability in degraded signal
environments such as in urban areas and indoors. Since the position accuracy yielded by
GPS methods is better than other positioning technologies, most wireless carriers are
looking at AGPS as the solution to meet the FCC criteria.
In this thesis, a GPS RF signal simulator was used to assess the signal acquisition
and tracking capability of a representative AGPS receiver. Extensive testing was
performed to analyze the effect of assistance data on AGPS acquisition and tracking.
Furthermore, performance of AGPS under various types of RFI and user dynamics was
investigated.
7.1 Conclusions
The following conclusions can be drawn from the work presented in this thesis:
• The AGPS receiver provided a 13 dB improvement in acquisition sensitivity
compared to an HSGPS receiver such as the SiRFXTrac and a 20 dB
improvement over a conventional receiver. No improvement was noticed in
tracking performance between the AGPS and HSGPS receivers. It was concluded
that aiding provides “coarse” estimates intended to assist acquisition and this
“coarse” assistance is not useful in improving tracking performance.
• In terms of assistance data, many interesting results were observed with the AGPS
receiver. For weak signals, timing accuracy had a significant effect on TTFF, with
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more accurate timing leading to lower TTFF values. However, there was a trade-
off between TTFF and position accuracy with more accurate timing leading to
larger positioning errors due to the inaccuracy of the first fix.
• With the initial position, as the user to reference distance increases, the TTFF also
increases. In terms on position uncertainty, it was found that if the horizontal
uncertainty was within the user to reference distance, the TTFF remains
unchanged. For a horizontal uncertainty higher than the user to reference distance,
the TTFF increases. No clear trend was observed in the position domain.
• As for position uncertainty, it was found that as the horizontal uncertainty remains
within the user to reference offset, there is no observable difference in TTFF.
Once again, no clear trend was observed in position accuracy.
• It was found that the AGPS receiver was able to tolerate 5 to 10 dB more
interference power than an HS receiver and 10 to 15 dB more than a conventional
receiver in acquisition mode. Both AGPS and HS had similar performance while
tracking. In all RFI cases, all the receivers tested (SiRFLoc, SiRFXTrac, SiRF
Standard and the OEM4) were able to tolerate more interference while tracking
than in acquisition mode.
• For an AGPS receiver, increasing velocity had no effect on TTFF performance
but the position accuracy decreased. The TTFF is at least four times lower with
the AGPS receiver over the HSGPS receiver. With an HSGPS, the TTFF
increased as the velocity increased and the position accuracy decreased. The
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velocity errors increased with increasing velocity during both acquisition and
tracking.
• When tested with different accelerations, the TTFF remained the same for an
AGPS receiver while it increased for an HSGPS receiver. In terms of position, the
accuracy of the position decreased for both AGPS and HSGPS receivers with
increasing acceleration, whether in acquisition or tracking mode.
7.2 Recommendations for Future Work
The following recommendations are made to improve the work presented in this thesis:
• All of the tests were conducted without the addition of many errors including
multipath, which is a major cause of error in real-life situations especially for high
sensitivity and AGPS receivers. Testing needs to be conducted to assess the
impact of multipath on AGPS acquisition and tracking performance.
• The advantage of hardware simulator testing is that it easier to isolate the
variables of interest and perform multiple, repeatable tests. However, ultimately,
the users of AGPS will be using their cellular phones in the outside world.
Therefore, field tests should be performed to assist in the prediction of real-life
AGPS performance from the simulation tests. Some field test results investigating
the effect of aiding parameters on AGPS have been published in Singh et al.
[2005]. The field results are similar to the results found in this thesis. But more
field tests can be performed in terms of interference and with user dynamics.
126
• When performing testing, the environment was not taken into consideration. A
few tests should be performed in a variety of environments. Indoor signal
replication including the effects of multipath has been performed by Hu et al.
[2005]. This work involved collecting field data from an HSGPS receiver and
reproduced the results using a hardware simulator. Similar tests can be performed
with an AGPS receiver.
• The characterization of an AGPS system was done using only one AGPS receiver.
Conducting the tests performed in this thesis with another AGPS receiver may
lead to different results. However, the conclusions drawn from the research will
remain the same.
• The effect of vertical uncertainty needs to be investigated.
With the arrival of Galileo, the concept of Assisted GPS is moving
towards Assisted GNSS (A-GNSS). The Galileo system proposes several free signals,
E5a (1176 MHz) and E5b (1207 MHz) as well as a narrower signal based on BOC (1,1)
modulation in L1 with the same central frequency as GPS L1 [Monnerat et al., 2004].
The Galileo L1 signal offers many advantages particular for LBS applications. Firstly, the
signal power is higher for Galileo, about 5 dB above GPS L1. Secondly, the signal design
calls for a dataless channel which removes the coherent integration limitation of 20 ms
[ibid].
The first modernized GPS satellite, IIR-M, was launched in late September 2005.
It has the capacity to implement the new military signals as well as the second civilian
signal, L2C. One of the main advantages of the L2C is that it has 45 dB cross-correlation
127
protection compared to 21 dB for L1 C/A-code. Better cross-correlation properties help
L2 receivers reject narrowband interference signals [Fontana et al., 2001]. The most
inherent advantage of the modernized signals and Galileo is the increase in satellite
availability which will improve poor availability currently experienced in urban canyon
environments.
Other developments in indoor positioning include techniques that do not use GPS
methods. Chun et al. [2005] discusses the potential of using wireless local area networks
(WLANs) to achieve indoor positioning by using reference points to estimate errors
between access points and the user. Also, there are some new techniques related to using
digital television (DTV) signals in obtaining user location information. The Rosum TV-
GPS combines broadcast TV signals with GPS to provide reliable indoor/outdoor
coverage [Rabinowitz and Spilker, 2003]. Therefore, one can safely say that
improvements in outdoor/indoor positioning will continue, with or without the use of
GPS. In the near future, however, GPS is here to stay and will be augmented using
different methods including the above mentioned WLANs and television signals.
128
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Appendix A: Network-based Positioning Technologies
The FCC E-911 mandate and location-based services have been the major driving forces
for cellular network operators to locate mobile users. There are many ways to locate a
mobile terminal. These techniques can be categorized as network-based techniques,
handset-based techniques or hybrid techniques.
The network-based techniques carry out the measurements on the network side
and consequently, modifications are necessary. The techniques belonging to this category
include Cell-ID, Angle of Arrival (AOA), Timing Advance (TA) and Time of Arrival
(TOA); these methods are discussed here.
Cell-ID
The simplest method for locating the mobile terminal is the Cell-ID solution. When a
mobile phone is switched on, it makes contact with a Base Transceiver Station (BTS) in
its vicinity; therefore, all mobile phones are automatically located by the network with an
accuracy of the radius of the connected cell. Depending on the transmission power of the
BTS, the cell radius varies from a few hundreds of metres in urban areas to as large as 35
kilometres in rural areas for the GSM system [Silventoinen and Rantalainen, 1995]
Angle of Arrival
The Angle of Arrival (AOA) technique is based on the signal directions measured at
multiple BTSs. In order to determine the signal direction, an antenna array is needed at
137
the BTS. Therefore, major modifications at the BTS are required. However, smart BTS
antennas will be used in the future 3G system, AOA measurements may be available
without additional antenna array [Chen, 2002].
.
Timing Advance
The Timing Advance (TA) technique is based on the existing TA parameter in the GSM
network. The TA parameter, which is determined by the network, is a round trip
propagation delay of the signal between the mobile terminal and the serving BTS. The
serving BTS measures the round-trip delay of the access bursts carried in the Slow
Associated Control Channel (SACCH). The error of the distance derived from the TA
parameter is about ± 550 metres in the GSM system. Three TA parameters are needed in
order to determine a 2D-position. Therefore, the network has to perform two or more
positioning handovers in order to obtain the additional two or more TA measurements
from the neighbour BTSs. If the mobile phone is in idle mode, the network needs to
establish a call to the mobile terminal in order to determine the TA parameters [Chen,
2002].
.
Time of Arrival
Time of Arrival (TOA) positioning method is based on measuring the time of arrivals
from known uplink bursts (from MS to different BTSs). At least three measurements are
needed for positioning. TOA system needs a synchronous network system, which means
that TOA cannot be used in GSM without modifications. The TOA method used in GSM
is called Time Difference of Arrival (TDOA) [Kinnari, 2001].
138
The values needed in TDOA method are TOA measurements from a Location
Measurement Unit (LMU), real time difference (RTD) measurement, also from LMU,
and the coordinates of the BTSs. RTD values are needed to remove the time difference
between asynchronous BTSs in a manner similar to the way it is done in Enhanced
Observed Time Difference (EOTD). As a result, three TDOA measurements give two
hyperbolas, whose intersection is the position of MS.
When TOA measurement is needed, MS is forced to do an asynchronous
handover. In an asynchronous handover, MS sends 70 access bursts, which are used as
known bursts in TOA measurement, to its serving BTS and neighbour BTSs. The TOA
value is measured using all 70 access bursts to improve accuracy. The accuracy can also
be improved by removing multipath signals, which can be done, for example, using
LMU's multipath rejection techniques, antenna diversity and frequency hopping. The
accuracy of the TDOA positioning is close to accuracy in EOTD measurement,
approximately 50-100 m.
When an application wants to know the position of the MS, it sends a positioning
request to Serving Mobile Location Centre (SMLC). Depending on the accuracy level,
SMLC decides how many measurements are needed. SMLC receives the needed
information for positioning such as TOA and RTD measurements from LMUs and the
coordinates of the BTSs. With this information, SMLC calculates the position of MS and
sends it to the application [Syrjärinne, 2001a].
