Positioning using Signals- of Opportunity based on OFDM

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MAI 2011

Laboratoire de Traitement du Signal et des Télécommunications

Positioning using Signals-of Opportunity based on

OFDM

Olivier Julien, P. Thevenon (now CNES), D. Serant

Ecole Nationale de l’Aviation Civile, Toulouse, France, www.enac.fr

[email protected]

Funding from:

Colloquium on Satellite Navigation

TU München, May 23rd, 2011

mailto:[email protected]

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ENAC Field

Short Presentation of ENAC

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ENAC Key Figures

Initial Training: 1 700 students enrolled in 15 different cursus

(engineer/Master, technicians, air traffic controllers, pilots,

dispatchers)

6 "Mastère Specialisé" degrees (1 year) + 3 in China

2 Master of Science degrees (2 years)

Lifelong training: 5 000 attendants

Research and Innovation

International

– 6 000 foreign students trained

– 35 exchange agreements with universities


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ENAC Research and Innovation


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LTST (Signal Processing and Telecom Laboratory):

Human Resources (as of May 2011)

6 permanent staffs and 7 Ph.D. students

Fields of Research

– GNSS for Civil Aviation (integrity, augmentation, signal processing)

– Urban Navigation (high sensitivity, hybridization, integrity, SoO, precise

positioning)

– Precise positioning (RTK, PPP)

– GNSS Signal Design (satellite payload, modulation, navigation message)

– GNSS Software Receiver

Key elements

15 Ph.D. thesis defended, 3 post-docs

50+ expertise contracts, 100+ papers published

4 collaborative patents

ENAC Research in GNSS


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Context

Urban & indoor positioning represents a huge market:

– Regulatory incentives (E911)

– Convergence of telecommunication and localization services

– Military application

It is known that urban and indoor environment is challenging

for GNSS because of interference, signal blockage, multipath,

etc…

To deal with this challenge, specific GNSS developments have

been done (high-sensitivity, Aided-GNSS, system-level GNSS

upgrades). However, they only provide limited position

availability, accuracy, continuity in challenging environments

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MAI 2011


Context

Alternative solutions are linked to the use of systems/signals

that are complementary to GNSS:

– Other navigation sensors: Inertial sensors, magnetometers, Wheel

Speed Sensors, laser, video cameras, …

– Dedicated radio-location systems: pseudolites, RFID, UWB

– Systems of signals of opportunity (SoO) that are not meant for

positioning a priori: Mobile telephony (2G or 3G), TV, Radio, WiFi signals

SoO have several advantages, even if they are not meant

primarily for navigation:

– Availability in urban centers

– Plurality of potential systems

– Integration with telecommunication services

The presentation will look at a subset of SoOs that are based

on OFDM modulation

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Presentation Outline

1. Introduction to OFDM and test signals

2. Propagation channel-related issues for positioning

3. Positioning principle

4. Proposed pseudo-range estimation algorithms

5. Results on pseudo-range estimation

6. Results on positioning

7. Conclusion and future work

8. Publications on this topic

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MAI 2011


Frequency selectivity of multipath channel causes distortions

that degrade a wideband transmission performance

OFDM solution:– Transmit symbols on narrow-band orthogonal sub-carriers,

where channel distortion can be easily corrected.

– 1 symbol per sub-carrier

– Implemented by iFFT / FFT in DSP

– A guard interval called Cyclic Prefix (CP) is

introduced to avoid Inter-Symbol Interferences and

allows demodulation in loose synchronization

conditions. The CP is the replica of the end of the

OFDM symbol

OFDM Principle

0 200 400 600 800 1000 12000

0.2

0.4

0.6

0.8

1

1.2

1.4

Spectrum of orthogonal subcarriers

OFDM symbol k OFDM symbol k+1 CP CP …

CPN samples CPN samples

0 200 400 600 800 1000 12000

1

2

3

4

5

Channel effect on the subcarriers

Multipath channel

spectrum

1- Introduction to OFDM and Test

Signals

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Modulator

Demodulator

OFDM Modulator and Demodulator

ns

pd

ic

ic : Tx symbolsns

)(nciFFTs in

: Tx samples

Serial

to

Parallel

IFFT

Parallel

to

Serial

Modulator

Add CP

Parallel

to

Serial

FFT

Serial

to

Parallel

Demodulator

Remove

CP

nr

nr : Rx samplespd

)( prFFTd np

: Demodulated symbols


Signals

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Synchronization chain:

OFDM Transmission Model

ModulatorDAC

Ts

Multipath

channel ADC

Ts’

fc fc’

Synchronization

& demodulator

Timing

Synchronization

Frequency

Synchronization

Sampling Clock

Synchronization

FFT windows

positioning

Frequency

CorrectionResampling Demodulator

Rx

samples

Emitter Receiver


Signals

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OFDM pilot sub-carriers In order to be able to correct the distortion brought by the

channel, pilot symbols are introduced among the transmitted

data.

– Known symbols that can be compared to the corresponding

demodulated symbols for channel estimation.

Possibility to obtain the channel impulse response (CIR) for

equalization.

Ex: Pilot distribution in the DVB-SH/DVB-T standard:

Frequency (subcarriers index)Tim

e (O

FD

M s

ym

bo

ls)

Continuous pilots

Scattered pilotsData & TPS


Signals

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OFDM and Single Frequency Network

A SFN is a network where all emitters emit the same signal, on

the same frequency.

They are used to

– reach users in zones that could be shaded if only one powerful emitter

was used,

– to ensure smooth transition between emitters.

OFDM is well adapted for SFN thanks to the use of circular

FFTs, of the CP and of narrow sub-carriers. However, to be

useful, the delay spread of all received signals should be within

the CP duration very stringent emitters’ synchronization

Emitter synchronization is very interesting for positioning when

using multilateration: there is no need for station monitoring the

emitter clock drift + the emitter synchronization is usually done

with respect to the GPS time


Signals

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European Digital terrestrial TV standard, worldwide adopted

Base for the European mobile TV standards DVB-H (Handheld) and DVB-SH (Satellite-to-Handheld)

Multi- or single frequency networks (MFN/SFN) are possible

Interest for urban/indoor positioning:– High power signals → potentially high availability (even indoor) of the

positioning service

– Deployed in VHF and UHF bands, signal is less attenuated by walls than in GNSS band

– Fixed emitters

– Large signal bandwidth: good for

precise synchronization

– SFN

It is already available

The DVB-T standard


Signals

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European digital mobile TV standard

Emitters network composed of

– A network of terrestrial emitters

– One or several GEO satellites

Possibility of SFN

Interest for urban/indoor positioning:

– Dense coverage in urban centers

– Wideband signals (up to 8 MHz is foreseen)

– SFN

– Convergence with other telecommunication systems

The DVB-SH band is at 2.2 GHz, close to other telecommunication bands

(eg 3G band at 2.1 GHz, WiFi band at 2.45 GHz).

The DVB-SH standard

Frequency sharing between satellite and terrestrial repeaters


Signals

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Terrestrial network propagation issues

The presence of multipaths

– Fast and strong fading of the

received signal power for short

multipath delays,

– Multiple replicas of the signal for

large multipath delays,

Large average power decay vs

distance created by the overall

environment.

Masking / blocking of the line-

of-sight (LOS)

– NLOS multipath may be received

with a stronger power.

0 0.2 0.4 0.6 0.8 1-25

-20

-15

-10

-5

0

5

Time (s)

Ta

p a

mp

litu

de (

dB

)M

ultip

ath

am

plit

ud

e (

dB

)

Time (s)

Multip

ath

dela

y (

s)

0 0.2 0.4 0.6 0.8

-0.5

0

0.5

1

1.5

2

2.5

3

x 10-6

2- Propagation channel-related issues

for positioning

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SFN propagation channel issues Delay overlap

– At certain locations of the coverage called iso-delay

zones, signals from different emitters may arrive

simultaneously.

Emitter identification issue.

Near-Far Effect (commonplace in all terrestrial networks)

– A signal received from a closer emitter will have a

significantly stronger power than from remote emitters

and may prevent their detection.

Emitter detection issue (>3 required for 2D positioning).

LO

S


for positioning

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Example of SFN CIR measurements No model taking into account all these characteristics was

found, in particular for the fine multipath delay modeling

Use of Channel Impulse Response (CIR) measurements.

CNES conducted a channel sounding campaign to

characterize the SFN CIR in urban environment (DVB-SH).

– 2 terrestrial emitters +1 helicopter acting as a GEO sat emitter

– Urban + dynamic


for positioning

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The multi-lateration principle is used (as in GNSS)

– Emitters’ locations are known.

– Tight synchronization of the emitters is required.

– The Line-of-Sight transit time from an emitter to the receiver is

measured, thanks to receiver synchronization processes.

– A minimum of 3 timing measurements are required for 2D positioning.

Can be done:

– with Time of Arrival

– with Time Difference of Arrival

Positioning principle

3- Positioning Principle

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Positioning principle using SFN Hypotheses

– Emitters’ locations are known: Terrestrial emitters are fixed (for DVB-SH,

the geo-stationary satellite should provide precise orbit information)

– Tight emitter synchronization:

Current SFN deployments: ±1 µs / 300 m

Current state-of-the-art: ±50 ns / 15 m

Assumption here: perfect sync or clock correction (currently under analysis

on DVB-T emitters)

Challenges

– Emitter identification in SFN: Same signal emitted synchronously from

every emitter.

– Fine pseudo-range estimation using OFDM signals in urban

environment:

OFDM does not need fine synchronization for telecommunication.

Urban environment creates NLOS bias and heavy multipaths.


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Proposition for solving the SFN issues Introduction of artificial delay

– One delay per emitter

– Known by the receiver

– Does not degrade data demodulation

if the overall delay is inferior to the CP length

Avoids the delay overlap issue;

Mitigates the near far effect;

Enable emitter identification.

Artificial

delay

𝑟(𝑡) = 𝑠(𝑡 −𝑑𝑘

𝑐− 𝜏𝑘). 𝑙 𝑑𝑘

𝑁𝑇𝑥

𝑘=1

received signal pathloss to emitter k

transit time

to emitter k

artificial delay

of emitter k


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Emitter identification in SFN

Emitter identification by reverse positioning

– Thanks to the introduction of artificial delays, only one combination of

pseudo-range measurements should correspond to a given location.

– With sufficient a priori knowledge, it is possible to find the association

between a set of anonymous pseudo-ranges and their emitter of origin.

– The required a priori information is an approximate position and the

characteristics of the present emitters

Mathematical formulation

– A cost function is defined

– All combinations of association between measurements and emitters

are tested. The one giving the smallest cost function corresponds to the

estimated emitter identities.

𝑉(𝑝, 𝑝 ,𝛔) = 𝜌𝑖 − 𝑑𝑖 − 𝑐. 𝜏𝑖 2

𝑁𝑇𝑥

𝑖=1

approximate position PR model taken at 𝑝

true position

tested permutation

PR measurement


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Emitter #1

Emitter #2

Proposed Estimation Technique

Conventional techniques(used for telecom)

Proposed techniques(specifically designed for positioning)

t

1. CIR estimation

2. Multipath delay acquisition:

– Extraction of the delay of the

main multipath components in the

CIR

3. Multipath delay tracking:

– parallel tracking of previously

acquired delays

4. Pseudo-range calculation:

– selection of the earliest estimated

delay for each emitter

Coarse syncOFDM

Demodulation

Multipath delay

acquisition

Multipath delay

tracking

Pseudo-range

calculationCIR estimation

4- Proposed Pseudo-Range

Estimation Technique

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CIR estimation by correlating the received signal with a local

replica containing the pilot sub-carriers only

The correlation peak’s sidelobes can mask the correlation

peaks from the other emitters.

Coarse syncOFDM

Demodulation

Multipath delay

acquisition

Multipath delay

tracking

Pseudo-range


CIR Estimation

Emitter #2

Emitter #1

Sat Emitter



Ex: CNES DVB-SH CIR

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CIR Estimation using Windowing

Windowing techniques are

used to reduce the sidelobes’

amplitude of the correlation fct.

– Use of rectangular, Hamming and

Blackman-Harris

The use of windowing

significantly reduces the near-

far effect problem:

– The satellite is visible and emitter

#2 is not shadowed

Drawback: the main lobe is

wider, meaning that the

detection of the peak location

will likely be less accurate.



Hamming

Blackman-HarrisEx: CNES DVB-SH CIR

Ex: CNES DVB-SH CIR

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From a CIR vector estimate, estimate the delay of a set of

multipath (Matching Pursuit, ESPRIT, etc…):1. Find the multipath with the highest amplitude

2. Subtract the multipath from the CIR estimation

3. Loop (1-2) with corrected CIR estimate

-1 0 1 2 3

x 10-6

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6x 10

-3

Multipath delay (s)

Estim

ate

d M

ultip

ath

am

plit

ude

-1 0 1 2 3

x 10-6

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6x 10

-3

Multipath delay (s)

CIR

am

plit

ude

Coarse syncOFDM

Demodulation

Multipath delay

acquisition

Multipath delay

tracking

Pseudo-range




Path Acquisition

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Multiple DLLs are launched from previously estimated delays

Discriminator: normalized |E|²-|L|²

Tracking loop: 2nd order, with 10Hz of noise loop equivalent

bandwidth

Sensitivity around 20 to 30 dB below

demodulation threshold

Tracking error in AWGN conditions

(no multipaths)

– Sub-meter accuracy for low SNR (> -10 dB)

– Tracking could be possible for remote

emitters or in indoor environment.

Coarse syncOFDM

Demodulation

Multipath delay

acquisition

Multipath delay

tracking

Pseudo-range




Signal Tracking

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Delay selection for pseudo-range

estimation

– Group the estimated delays by emitters (via

clustering techniques).

– Take the earliest delay for each emitter.

– BUT, we could lose the LOS signal or even not

track the LOS at all

Pseudo-range estimation strategy

– New acquisitions are periodically launched

– DLLs are stopped if converging towards the

same delay, or tracking a weak part of the CIR.

Coarse syncOFDM

Demodulation

Multipath delay

acquisition

Multipath delay

tracking

Pseudo-range




Pseudo-Range Calculation

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Semi-Simulated data

FFT size – NFFT 2048

CP length 1/4

Signal bandwidth B 5 MHz

Emitter EIRP 53.2 dBm [12]

Noise floor level -102.6 dBm [10]

SNR Between 9.2 - 49.2 dB

Noise equivalent loop bandwidth Bl 10 Hz

Time between 2 tracking updates Ti 448 µs

Time between 2 acquisition phases TACQ 14.336s

Acquisition threshold -120 dBm

Tracking threshold -140 dBm

Clustering threshold 2.5 µs

Max number of DLL launched after each acquisition 15

• The proposed pseudo-range

estimation technique was applied

to the CNES SFN measurements

including artificial delay.

– Moving van in urban environment

– 3 emitters

– 1000s of CIR measurements

• The simulation parameters were

chosen based on early

publications on DVB-SH system

deployments.

5 – Results on Pseudo-Range

Estimation

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Simulated data: Hamming window

• Result analysis:

– Clustering is working:

3 delay clusters, 1 for

each emitter

– Tracking of sidelobes

especially for emitter

#1 (improved

compared

to rectangular

window)

– Unavailability of

Emitter #3Availability

(%)

Mean error (m) Error standard deviation (m)

overall median overall median

Emitter #1 100 -150.2 -0.06 219.1 2.5

Emitter #2 100 26.7 0.41 61.9 3.9

Emitter #3 (sat) 88.6 29.7 0.02 169.0 1.3

5 - Results on Pseudo-Range

Estimation

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MAI 2011


Simulated data: Blackman-Harris

Availability

(%)

Mean error (m) Error standard deviation (m)

overall median overall median

Emitter #1 98.6 35.0 8.3 80.5 32.4

Emitter #2 92.9 62.0 70.4 79.7 51.1

Emitter #3 (sat) 100 0.003 0.000 0.9 0.7

• Result analysis:

– Tracking of sidelobes

solved

– Availability of Emitter

#3

– Frequent leaps

between first and

second multipath for

Emitter #2


Estimation

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Test on DVB-T (already available)

2 devices:

– A GPS receiver for time reference,

– An USRP2/WBX for TV signal down-conversion and digitization.

Recording a signal from

a French digital TV

emitter:– EIRP of 5kW,

– distance 80 km,

– frequency 554 MHz

No reference distance is

used (only PR variations

are investigated)

Real Signals: Test Bench Description

TV Antenna

Gain : 20 dB

NF : < 3.5 dB USRP2/WBXGain: 0 – 31.5 dB

LO: 50 MHz – 2.2 GHzFs : 100MHz/k k=[4..512]

GPS Active

Antenna L1/L2

Gain : 20 dB

NF : < 3.5 dB

GPS receiverNovatel OEM4

Channel I

Channel Q

To PC for recording

10MHz ref. locked on GPS time


Estimation

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The correlation image illustrates the evolution of the absolute

value of the correlation function over time.

In both cases: 2 strong peaks spaced by 4 km.

In the indoor case: higher noise floor and more multipaths

Uncertainty in knowing if this is the direct signal

Real Signal Test – Static Scenario

2 4 6 8 10 12

-2

0

2

4

6

Time (s)

Dela

y (

km

)

-30

-25

-20

-15

-10

-5

0

10 20 30 40 50 60

-2

0

2

4

6

Time (s)D

ela

y (

km

)

-30

-25

-20

-15

-10

-5

0

Outdoor case Indoor case

Colo

r s

cale

in d

B

Colo

r s

cale

in d

B


Estimation

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Outdoor case Indoor case

• A zoom on the main signal shows a very difficult environment

indoor. Still the strong transmitted signal ensures availability,

but with an unknown precision

10 20 30 40 50 60

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Time (s)

Dela

y (

km

)

4 5 6 7 8 9 10

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

0.3

0.4

Time (s)

Dela

y (

km

)5 - Results on Pseudo-Range

Estimation

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Theory developped in [PLANS2010] : D. Serant et al.“Development and validation of an OFDM/DVB-T

sensor for positioning”, IEEE/ION PLANS, 4-6 May 2010

Tracking with a 1 Hz loop bandwidth:

The tracking error STD is quite low when considering the environment.

Note that results show only the tracking error STD but a bias can be

present if a reflection is tracked instead of the direct signal.


Outdoor case Estimated SNRPseudorange

Error Std Dev

Theoretically

predicted result*

First peak -3.5 dB ~8 cm ~3 cm

Second peak -9 dB ~8 cm ~6 cm

Indoor case Estimated SNRPseudorange

Error Std Dev

Theoretically

predicted result*

First peak -13 dB ~0.7 m 0.1 m

Second peak -25 dB ~2.3 m 0.5 m


Estimation

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20 40 60

-0.2

0

0.2

0.4

0.6

0.8

Time (s)

Del

ay (

km

)

-30

-20

-10

0

This test is a succession of static and dynamic phases as described in the following table:

Real Signal Test – Dynamic Scenario

Phase Static Dynamic Static Dynamic Static

Duration 15 s 15 s 15 s 12s 15s

0 20 40 60-60

-50

-40

-30

-20

-10

Time (in sec)

Est

imate

d S

NR

(in

dB

)

Correlation image Estimated SNR

During dynamic phases the correlation is disturbed and the estimated SNR drops.


Estimation

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Estimated pseudorange variation (1 Hz loop bandwidth):

WARNING: It is not the pseudorange error that is plotted but only its variation

since no reference position was recorded during the test.

Real Signal Test – Dynamic Scenario

0 10 20 30 40 50 60 70-30

-20

-10

0

10

20

30

Time (in sec)

Est

imat

ed P

seudora

nge

var

iati

on(i

n m

)

• During the static phases: the

pseudorange variation is quite stable.

• But during dynamic phase the

pseudorange estimation is clearly

disturbed

-> the absence of reference position and

the limited traveled distance during the

test do not permit to give a quantitative

performance in dynamic scenarios.


Estimation

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MAI 2011


Conclusion on PR Estimation

Error on the pseudo-range estimation is affected by:

– A potential unavailability of PR due to the Near-Far effect. Reduced

when using windowing.

– Numerous steps of the pseudo-range errors due to the propagation

channel.

– A large error for the terrestrial emitters due to the tracking of strong

sidelobes or multipath. This can be due to signal blockage.

Good performance for slices of the time series

– Numerous jumps (several hundreds of meters)

– If detected, PR estimation performances could reach meter-level

To be published soon:

– Promising work in reducing measurement jumps

– Test set up has been significantly improved (multi-receiver + mobile test

bench) with availability of reference distance


Estimation

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Position-domain simulation The results are shown only using the semi-simulated CNES

SFN measurements

The PR error calculated previously was done between the

simulated delay estimate and the delay provided by the

proposed tracking technique.

However, the CNES measurements may be affected by an

unknown NLOS bias.

In order to observe this phenomenon, the position was

computed using the estimated pseudo-ranges:

– Conventional Non-Linear Least Square algorithm;

– Position averaged over 1s.

Comparison between these DVB-SH tracks and the GPS track

recorded during the CNES measurement.

6 – Results on Position Estimation

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With Ideal PR estimates Heavy NLOS bias present even with perfect PR estimates

Mean absolute error at 24.3 m (whole time series).


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With Best PR estimate combination

Best combination

– 2 PR estimates (emit #1 and #3) from Blackman-Harris simulation

– 1 PR estimate (emit #2) from the Hamming simulation

Mean absolute error at 76.8 m (whole time series)

41.9 m (first 800 s)


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Conclusions The main contributors to the position error are

– The NLOS bias

– The pseudo-range estimation bias due to the tracking of multipath

The best achieved performance is a mean absolute error

around 40m

– Considering only 3 emitters

– Considering heavy multipaths

Performance is improved when combining different windowing

techniques.

To be published:

– Test with real DVB-T signals

– Hybridization with GNSS


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Conclusions

The overall results have demonstrated the feasibility of

autonomous positioning using a dense terrestrial network of

emitters transmitting OFDM-based signals:

– System level modification to permit emitter identification in an SFN;

– Emitter identification technique working in SFN;

– Pseudo-range estimation technique working with OFDM signal, in urban

environment.

The methods were validated by simulation using realistic

propagation channel measurements provided by CNES.

– The best positioning performance is around 40 m (mean absolute error)

with the use of only 3 emitters and a moderate signal bandwidth.

The proposed methods were shown not impact the provision of

TV broadcast if the right signal parameters are chosen (not

shown in this presentation)

7 – Conclusions and Future Work

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Future work The first results look promising. However, improvements can be

expected and are all underway:

– Improving the pseudo-range estimation method and avoiding the

measurement jumps (currently underway)

– Doing extensive real-world measurements to obtain a valid PR error

model

– Implementing an elaborate position calculation technique

Finally, most results are applicable to other OFDM, SFN-based

standards

– Terrestrial digital radio / TV broadcast: DAB, DVB-x, DMB-x, ISDB

– Mobile communication: 3GPP LTE, mobile WiMAX

7 – Conclusions and Future Work

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More information Thevenon P, Julien O, Macabiau C, Serant D, Ries L, Corazza S., Bousquet M.,

Positioning principles with a mobile TV system using DVB-SH signals and a Single Frequency Network. In: 2009 16th International Conference on Digital Signal Processing. IEEE; 2009. p. 1-8.

Thevenon P, Julien O, Macabiau C, Serant D, Corazza S, Bousquet M., Pseudo-Range Measurements Using OFDM Channel Estimation. In: ION GNSS 2009; 2009. p. 481-493.

Thevenon P., S-Band Air Interface for Navigation System: A focus on OFDM Signals, University of Toulouse, PhD Thesis, 2010

Thevenon P., S. Damien, O. Julien, C. Macabiau, M. Bousquet, L. Ries, S. Corazza, Positioning Using Mobile TV Based on the DVB-SH Standard, to be published in Navigation: the Journal of the ION.

Serant D., Julien O, Macabiau C, Thevenon P, Dervin M, Corazza S, M.-L. Boucheret, Development and Validation of an OFDM/DVB-T Sensor for Positioning. In: IEEE/ION PLANS 2010; 2010. p. 988-1001.

Serant D., Julien O, Macabiau C, Thevenon P, Dervin M, M.-L. Boucheret, Use of OFDM-based Digital TV for Ranging: Tests and Validation on Real Signals, NAViTEC conference, 2010.

Serant D., O. Julien, C. Macabiau, P. Thevenon, M. Dervin, L. Ries, M.-L. Boucheret, Positioning Using OFDM-based Digital TV: New Algorithms and Tests with Real Signal, accepted for ION GNSS 2011

8 – Publications on this Topic

Documents

Positioning using Signals- of Opportunity based on OFDM