GNSS PRECISE POINT POSITIONING WITH ANDROID … · Huawei introduced the Android 9 Mate 20X in late 2018 with the capability of recording code, ... F antenna as is the case for other

IEEE International Conference on Signal, Information and Data Processing 2019, Chongqing, China, 11-13 Dec 2019 1

GNSS PRECISE POINT POSITIONING WITH ANDROID

SMARTPHONES AND COMPARISON WITH HIGH PERFORMANCE

RECEIVERS

Gérard Lachapelle and Paul Gratton

Department of Geomatics Engineering University of Calgary

Calgary, Canada

Abstract— Precise Point Positioning (PPP) is becoming increasingly used instead of differential GNSS(DGNSS) due to its ease of use. With PPP, precise satellite orbits and clock corrections are calculated using the numerous International GNSS Service (IGS) permanent stations. The IGS network conceptually replaces the reference station(s) used in DGNSS. Models of the ionosphere and the troposphere are used to aid PPP, especially ionospheric models for single frequency users. In addition to 3D position, PPP provides estimates of GNSS time and zenith tropospheric delays.

PPP performance is analysed herein as a function of receiver type, observation time and measurement utilized. The high-end receivers used in this study are multi-frequency multi-constellation Leica GS16. The Android phone used in the new Huawei Mate 20X. The measurements that are intercompared are (1) single frequency code, (2) single frequency code and carrier phase, (3) dual frequency code, and (4) dual frequency code and carrier phase. Results in low and high multipath environments are reported. Focus is on the use of GPS and GLONASS constellations because most IGS stations are equipped with such receivers, which is necessary to calculate precise satellite orbits and clock corrections. In order to assess PPP versus DGNSS performance, the results of a test consisting of an array of receivers are reported and analysed.

Keywords—DGNSS, PPP, carrier phase, Huawei Mate 20X, positioning

INTRODUCTION

Huawei introduced the Android 9 Mate 20X in late 2018 with the capability of recording code, carrier phase, Doppler and C/No measurements every second on GPS L1 & L5, GLONASS L1, Galileo E1 & E5a, Beidou and QZSS L1 & L5. A picture of the unit is shown in Figure 1. Two units were procured (labeled 20XB and 20XR) for the evaluation described here to verify repeatability between units. Since performance was the same for both units, results are provided indiscriminately for either one or the other. The 20X is equipped with a planar inverted-F antenna as is the case for other smartphones, a well-known limitation for GNSS measurements that results in high code measurement noise and multipath. The chip used by the unit is the HiSilicon Kirin 980 [https://www.gsmarena.com/huawei_mate_20_x-9369.php]. The data logger used for the static and kinematic reported in the sequel is the Geo++ RINEX 2.1.1. PPP post-processing was performed with Natural Resources Canada’s (NRCan) CSRS-PPP 2.26.1 online software that can process GPS, GLONASS or GPS-GLONASS L1/L2 data in either static or kinematic mode [https://webapp.geod.nrcan.gc.ca/geod/tools-


outils/ppp.php?locale=en]. The CSRS-PPP has been available online since the early 2000s (Mireault et al 2003) and utilized by thousands of users. The inclusion of Galileo and Beidou data is not yet possible. Hence the PPP results described below are based on GPS and GLONASS L1. Differential GNSS (DGNSS) performance evaluation was conducted using RTKLib, a well-known open source software. Comparisons were made with the results of two high end Leica GS16 (labeled GS16B and GS16R) receivers. Given the high cm-level accuracy of the latter, their positions served as reference to evaluate Mate 20X derived positions.

Figure 1: Huawei Mate 20X Pro

PPP AND DGNSS FUNDAMENTAL METHODOLOGIES AND ALGORITHMS

These methodologies and algorithms are well known and ample literature is available for both methods (e.g. Kouba, 2015, Kouba et al, 2017, Odijk & Wanninger 2017). A summary of PPP is provided below. The undifferenced code and carrier phase measurement equations in unit of length outputted by a receiver for each frequency can be written as

𝐏 = 𝐜𝐝𝛕 = 𝛒 + d𝝆 + c(dt-dT) + dion + dtrop +𝜺P

𝜱 = 𝛒 + d𝝆 + c(dt-dT) + 𝝀N - dion + dtrop +𝜺𝜱

where

P Code (also called pseudorange) measurement (time of transit of signal 𝜏 multiplied by speed of light c in vacuum

carrier phase measurement (number of cycles x frequency wavelength)

wavelength of frequency used (e.g. about 20 cm for L1/E1)

N cycle ambiguity (integer number for single frequency measurements) - Difficult to determine – often estimated as a non-integer number (e.g Teunissen 2017)

ρ geometric range (i.e.|| rs – Rr || ) obtained from estimated orbits

dρ orbital errors

rs, Rr position vector of SV (known) and rx (unknown)


dt satellite clock offset from GNSS time, including satellite code biases & delays

dT receiver clock offset from GNSS time, including receiver code biases & delays

dion ionospheric delay

dtrop tropospheric delay

εP code noise {function of ε(code noise) and ε(code multipath)}

ε (C/A code noise) ≈ 5 – 200 cm for LOS measurements

ε (code multipath) ≤ 0 to 10s of metres (non-Gaussian)

noise {function of ( noise) and ( multipath)}

( noise) 1-5 mm (Lower than code measurements)

( multipath) ≤ 0.25 (Lower than code measurements)

combined effect of phase noise & multipath << corresponding code values

In PPP mode, precise orbits and satellite clock corrections generated by the International GNSS Service (IGS) network or a subset thereof are used; dρ (orbit error) and dt (satellite clock error) disappear from the above equations, leaving

P = cdt = 𝛒 - cdT + dion + dtrop + εP

𝜱 = 𝛒 - cdT + 𝝀N - dion + dtrop + ε

If multi-frequency measurements are used, dion disappears. The remaining unknowns are receiver coordinates and time offset, troposphere and (undifferenced) carrier phase ambiguities. The latter are resolved either as integer values or as real numbers. In the latter case, they remain parts of the state vector are updated at each measurement epoch, hence the term “float ambiguities”.

The tropospheric effect is traditionally expressed as its zenith value (which is of the order of 2.3m at sea-level). Since its behavior as a function of the satellite elevation angle is fairly well known and predictable, a mapping function is used to transfer the effect from its zenith to its inclined value or vice versa. The zenith value is referred to as the zenith tropospheric delay (ZTD or TZD). With precise multi-frequency “geodetic” receivers like the Leica GS16 used herein to assess Mate 20X performance, precise ZTD are obtained, a valuable derived measurement used by weather and climate experts. When single frequency measurements are used as is the case here with the Mate 20X, the ionospheric effect is estimated by a Global Ionospheric Model (GIM) based on a regional, continental or global reference GNSS network. The NRCan PPP software uses this method to predict the ionospheric effect for single frequency measurements. ZTD is estimated using either a Global Pressure and Temperature (GPT) model or a more precise model available in post mission like the Vienna Mapping Function 1 (VMF1) (Böhm et al 2007). NRCan provides different level of services from “Ultra Rapid” to “Final”. The differences have to do with the accuracy of orbital and satellite clock corrections (dρ and dt), and the GIM and ZTD models used. In the case of the results reported below, no significant differences were observed at the level of 5cm. The use of the VMF1 versus GPT for the Mate 20X results did not cause any significant differences either; the tests


were carried in the climate of the Calgary area at an elevation of about 1200m, avoiding the issue of water vapor variability. This is different in the case of points located near the ocean as shown by Banville et al (2019). The GS16 GPS and GLONASS data was processed with both L1 and L2 measurements in which case the ionospheric effect is derived directly from a linear combination of these measurements and the subsequent direct estimation of the ZTD.

Carrier phase measurements must be continuous or nearly continuous to be of values as losses of phase lock results in new ambiguities to be estimated. The Mate 20X carrier phase measurements were generally found to be of a high quality for this type of GNSS receiver as will be seen below.

TEST ENVIRONMENTS AND MEASUREMENT QUALITY ASSESSMENT

All measurements were made under open sky conditions to avoid attenuation caused by line-of-sight obstructions. Low multipath testing was achieved on open hill or mountain tops away from man-made structures and nearby trees. A flat roof on a four-storey building on campus of the University of Calgary provided high multipath conditions. Pictures of these two test environments are shown in Figure 2. Carrier-to-noise (C/No) values constitute a good metric of measurement quality and effect of multipath. These are shown on the upper two plots of Figure 3 for GPS L1 and L5 measurements and range between 25 and 43 dB-Hz, significantly lower than corresponding values of 45 to over 50 dB-Hz obtained with a GS16 geodetic receiver, the main reason being antenna limitations as discussed in the Introduction. Carrier phase measurements have low noise and are affected minimally by multipath as described in the previous section. Comparisons between carrier phase and code measurements, once phase ambiguities are removed, therefore provide a good measure of code accuracy. Such a code-minus-carrier (CMC) data sequence is shown in the lower plot of Figure 3. No Mate 20X cycle slip occurred over a period of 100 minutes. The mean standard deviation of 2.04m for numerous data sequences represents the combined effect of code noise and multipath in the low multipath environment of Figure 2. The corresponding results for the high multipath environment, not plotted here, show larger C/No variations, significantly higher numbers of cycle slips and code standard deviations of the order of 1.5 to 3 times those in low multipath conditions.

Figure 2: Low and high multipath test environments


Figure 3: Mate 20X C/No and CMC values under low multipath

In order to show Mate 20X antenna limitations, measurements were made using an external NovAtel 702-GG high grade antenna. Since the Mate 20X does not have an external antenna connector, it was placed inside a metallic box lined with RF absorbent material. The 702-GG antenna was connected to the box with its open wire re-transmitting inside. The method had been used previously and is described in more detail in Lachapelle et al (2018). Results are shown for low (three left plots) and high (three right plots) multipath in Figure 4. C/No are 7 to 8 dB higher, and combined code noise and multipath standard deviations reduced substantially. Significant improvements, not shown here, also occur under high multipath although some cycle slips persist.

The capability of GPS L1/L5 and Galileo E1/E5a code measurements to estimate ionospheric corrections using the Mate 20X and GS16 units is shown in Figure 5, including the differential code bias. Dual-frequency ionospheric corrections are obtained through a linear combination of two frequencies, increasing the measurement noise substantially in the process. This is evident for the case of the Mate 20X ionospheric corrections which have standard deviation values of 2.5 to 3.0m. Better values can be obtained by combining carrier phase measurements with the corresponding code values.


Figure 4: Mate 20 C/No and CMC values with external antenna under low and high multipath


Figure 5: Sample ionospheric corrections derived with the Mate 20X and GS16 with GPS and Galileo dual frequency code data

PPP AND DGNSS POSITION QUALITY PERFORMANCE

PPP Static Data Processing

Static measurements were made during multiple periods of several hours over several days at the low and high multipath sites shown in Figure 2. Four units were used, namely the two Mate 20X and two GS16. The Mate 20X data was segmented into observation periods of 15 to 60 minutes to assess accuracy as a function of observation time. The full GS16 data periods of four to six hours were processed without segmentation to provide reference coordinates accurate to 1 cm to assess the Mate 20X estimated coordinate accuracy. Results are summarized in Figure 6 for a test of six hours. The dots in the figure show the Mate 20X coordinate errors for observation periods of 15, 30 and 60 minutes. In a six-hour period, there are therefore 24, 12 and six 15, 30 and 60-minute periods; dots appear to be missing in some cases because they overlap. The RMS errors (RMS differences between Mate 20X and GS16 coordinates) do not exceed 1m under low multipath as shown in Table 1 for the height component, which is always the most difficult to estimate accurately. The RMS errors are


significantly higher (3m) for high multipath as expected. The results for high multipath with an external geodetic antenna, not shown here, are close to those under low multipath with the unit’s own internal antenna). The vertical error bars are the 2-sigma (95% confidence level) values outputted by the CSRS PPP software. These are pessimistic by a large factor. This is because the software was developed to process high accuracy receivers. This is shown by examining the height results of the Mate 20X and GS16 in Table 1. In this case, the GS16 was also segmented in observation periods of 15, 30 and 60 minutes to allow a comparison. The RMS errors, which are calculated by comparing numerous GS16 data segments are of the same order of magnitude, once multiplied by 2 to obtain the 95% confidence level, as the 2-sigma values estimated by the PPP software. This is not the case for the Mate 20X derived heights in which case the PPP software 2-sigma values are up to several times larger than the corresponding 2 x RMS errors.

Figure 6: PPP Mate 20X static accuracy as a function of time for high and low multipath with unit’s own antenna

Table 1: Mate 20X and GS16 height comparisons – Static PPP

Test Conditions

Obs. Time (min)

PPP – Mate 20X PPP – GS16

RMSE (m) Mean 95%

Uncertainty (m) RMSE (m)

Mean 95% Uncertainty (m)

Low Multipath

15 0.94 9.75 0.21 0.42

30 0.55 7.40 0.23 0.17

60 0.21 4.30 0.06 0.07

High Multipath

15 3.18 19.38 0.06 0.40

30 2.67 12.62 0.03 0.16

60 2.19 8.50 0.02 0.07


The difficulty of processing noisy Android phone static data was concurrently studied by Banville et al (2019). Mate 20X sample results from Figure 11 of that paper are shown in Figure 7. An NRCan internal version of the CSRS PPP software were used with different atmospheric models and stochastic weighting approaches to obtain these results which show that the current online software version is not yet optimized for noisy smartphone data and that significant improvements are possible.

Figure 7: PPP Mate 20X horizontal and vertical position errors as a function of observation time and different approaches (Banville et al 2019)

DGNSS Static Data Processing

Mate 20X L1 GPS data was also processed with RTKLib using its DGNSS static with the code and carrier phase measurement option. The addition of GLONASS with RTKLib was found to be an issue as it gave consistently poorer results than GPS only, hence the latter was used to obtain the results presented below. One of the nearby (metres away) GS16 units served as reference station. The data was also segmented in periods of 15, 30 and 60 minutes. The coordinate errors for each observation periods are shown in Figure 8 and compared with the corresponding PPP results for both low (upper two plots) and high (lower two plots) multipath cases. The height error statistics are given in Table 2. The DGNSS solution 95% errors obtained with RTKLib are too optimistic by a large factor while the opposite is true for the corresponding PPP solutions. The RMS errors calculated from the segmented data provide more realistic values. These are much better for the DGNSS case. The reason is that only common satellites are used in differential mode. This is not the case with PPP. The satellites tracked by the Mate 20X and GS16 are slightly different at time and different rejection criteria are likely used by both software. The difference is rendered worst by the high code noise of the Mate 20X.


Figure 8: Mate 20X static DGNSS versus PPP for low (upper plots) and high (lower plots) multipath

PPP Kinematic Data Processing

Several kinematic tests were conducted on an open highway using the receiver configuration shown in Figure 9. The two Mate 20X units were placed immediately under the vehicle sunroof in line with the two GS16 receivers mounted on the vehicle roof. The offsets between all units were measured and applied prior to comparison. Speed was about 100km/h. The measurements were post-processed in PPP kinematic mode, using L1 code and carrier phase data for the Mate 20X and corresponding L1/L2 data for the GS16. An 8-minute section of the


results are shown in Figure 10. The vehicle went under overpasses at the epochs where coordinates discontinuities occurred. Otherwise, the coordinate inter-comparisons between the various units show carrier phase continuities for all units. The GS16 trajectory discontinuities when going under the overpasses are relatively small as expected as their low noise code measurements is effective in recalculating the correct phase ambiguities after loss of phase lock. In the case of the Mate 20X, the discontinuities are higher due to the much higher code noise and multipath effects; the magnitude of the trajectory segment differences might be due to incorrect integer ambiguity fixing, again due to high code noise and multipath. The reason why trajectory segments are smooth immediately after loss of phase lock is that measurements are post-processed in batch mode, resulting in smoothing benefits, and the carrier phase ambiguities were likely resolved as integer values.

Table 2: Mate 20X PPS versus DGNSS static heights solutions

Test Conditions

Obs. Time (min)

PPP – Mate 20X DGNSS – Mate 20X

RMSE (m) Mean 95%

Uncertainty (m) RMSE (m)

Mean 95% Uncertainty (m)

Low Multipath

15 0.94 9.75 0.181 0.013

30 0.55 7.40 0.075 0.003

60 0.21 4.30 0.080 0.003

High Multipath

15 3.18 19.38 0.646 0.045

30 2.67 12.62 0.355 0.024

60 2.19 8.50 0.297 0.016

Figure 9: Receiver configuration for open highway kinematic testing


Figure 10: Mate 20X and GS16 kinematic test comparison

CONCLUSIONS

The static and kinematic results described here show that the Android phone tested delivers very good carrier phase measurement performance in terms of continuity and accuracy under low multipath line-of-sight conditions. Code noise and multipath effects remain an issue, common to all smartphones, due to antenna design restrictions. In PPP static mode and under low multipath line-of-sight conditions, the phone delivered coordinate accuracy of the order of 1 m (2-sigmas) using 30 minutes of data. This is a significant improvement over previous Android phone generations with raw GNSS data capability tested by the authors. The experimental use of an external antenna under high multipath shows that the use of a better antenna would deliver results similar to the those obtained under low multipath with the smartphone own’s antenna.

The PPP results described in the paper were obtain with the online version of the Natural Resources Canada’s CSRS-PPP 2.26.1 software. The study conducted by Banville et al (2019) in parallel with this one shows that, owing to the high code noise of smartphones, the current online version of the software is not optimized for the processing of this type of data. The substantial improvement obtained in the paper quoted above reveals that yet better performance than those reported in the present paper are possible.


REFERENCES

Banville, S, G. Lachapelle, R. Ghoddousi-Fard and P. Gratton (2019) Automated Processing of Low-Cost GNSS Receiver Data. Proceedings of Institute of Navigation GNSS+2019 conference, Miami, 16-20 September 2019, 17 pages.

Böhm, J., R. Heinkelmann, and H. Schuh (2007) “Short Note: A global model of pressure and temperature for geodetic applications,” Journal of Geodesy, Vol. 81, No. 10, pp. 679-683.

Kouba, J. (2015) A Guide to Using IGS Products, Updated in 2015, available for free on IGS.org

Kouba, J., F. Lahaye and P. Tétreault (2017) Precise Point Positioning. Chapter 25, P. Teunissen & O. Montenbruck (Eds) Handbook of GNSS, Springer.

Lachapelle, G., P. Gratton, J. Horrelt, E. Lemieux and A. Broumandan (2018) Evaluation of a Low Cost Handheld Unit with GNSS Raw Data Capability and Comparison with an Android Smartphone. Sensors, MDPI, 18, 4185; doi:10.3390/s18124185, 22 pages.

Mireault, Y., Tétreault, P., Lahaye, F, Héroux, P., and Kouba, J. (2008) “Online Precise Point Positioning,” GPS World, 19, 9, 59-64.

Odijk, D. and L. Wanninger (2017) Differential Positioning. Chapter 26, P. Teunissen & O. Montenbruck (Eds) Handbook of GNSS, Springer.

Teunissen, P. (2017) Carrier Phase Ambiguity Resolution. Chapter 23, P. Teunissen & O. Montenbruck (Eds) Handbook of GNSS, Springer.

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GNSS PRECISE POINT POSITIONING WITH ANDROID … · Huawei introduced the Android 9 Mate 20X in late 2018 with the capability of recording code, ... F antenna as is the case for other