Study on Multi-GNSS Precise Point Positioning Performance with Adverse Effects of Satellite Signals on Android Smartphone
<p>View of the experiment site. Left: global navigation satellite system (GNSS) antenna of the reference station on the reference point SYS1, working all days; right: the Huawei Mate30 is placed on the instrument base above the reference point SYS2 and secured with tape.</p> "> Figure 2
<p>Huawei Mate30 sky plot and the GNSS satellites <span class="html-italic">C/N</span><sub>0</sub> values of the geodetic receiver and Huawei Mate30. (<b>a</b>) Each color corresponds to the <span class="html-italic">C/N</span><sub>0</sub> range of GNSS L1 band signal. (<b>b</b>) The same color on the top and bottom represents the same GNSS satellite. The G, C, and R denote GPS, BeiDou (BDS), and GLONASS, respectively.</p> "> Figure 3
<p>Huawei Mate30 carrier phase cycle slip detection of GNSS L1 band observations. (<b>a</b>) GPS and GLONASS; (<b>b</b>) BDS. The extra cycle slips of GPS and BDS satellites detected by measurement-based polynomial fitting (MPF) were marked with red boxes.</p> "> Figure 4
<p>Difference <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>i</mi> </msub> </mrow> </semantics></math> of observed GPS, BDS, and GLONASS satellites in static data collected by the Huawei Mate30 and geodetic receiver. (<b>a</b>) Huawei Mate30 difference <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>i</mi> </msub> </mrow> </semantics></math> of GPS; (<b>b</b>) Huawei Mate30 difference <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>i</mi> </msub> </mrow> </semantics></math> of BDS; (<b>c</b>) Huawei Mate30 difference <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>i</mi> </msub> </mrow> </semantics></math> of GLONASS; and (<b>d</b>) geodetic receiver difference <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>i</mi> </msub> </mrow> </semantics></math> of GPS, BDS, and GLONASS.</p> "> Figure 4 Cont.
<p>Difference <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>i</mi> </msub> </mrow> </semantics></math> of observed GPS, BDS, and GLONASS satellites in static data collected by the Huawei Mate30 and geodetic receiver. (<b>a</b>) Huawei Mate30 difference <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>i</mi> </msub> </mrow> </semantics></math> of GPS; (<b>b</b>) Huawei Mate30 difference <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>i</mi> </msub> </mrow> </semantics></math> of BDS; (<b>c</b>) Huawei Mate30 difference <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>i</mi> </msub> </mrow> </semantics></math> of GLONASS; and (<b>d</b>) geodetic receiver difference <math display="inline"><semantics> <mrow> <msub> <mi>D</mi> <mi>i</mi> </msub> </mrow> </semantics></math> of GPS, BDS, and GLONASS.</p> "> Figure 5
<p>Pseudorange residuals of observed GPS, BDS, and GLONASS satellites in static data collected by the Huawei Mate30. (<b>a</b>) Pseudorange residual of GPS; (<b>b</b>) pseudorange residual range of GPS; (<b>c</b>) pseudorange residual of BDS; (<b>d</b>) pseudorange residual range of BDS; (<b>e</b>) pseudorange residual of GLONASS; and (<b>f</b>) pseudorange residual range of GLONASS. The value after the satellite pseudo random noise (PRN) number is the RMS of pseudorange residuals.</p> "> Figure 6
<p>Comparison of the static PPP errors with different combinations of GPS, GPS/BDS, and GPS/BDS/GLONASS observed by the Huawei Mate30 in three directions. (<b>a</b>) GPS; (<b>b</b>) three-dimensional deviation points of GPS. (<b>c</b>) GPS/BDS; (<b>d</b>) three-dimensional deviation points of GPS/BDS. (<b>e</b>) GPS/BDS/GLONASS; (<b>f</b>) three-dimensional deviation points of GPS/BDS/GLONASS. The E and N denote east and north direction. The EK denotes the conventional PPP method with an elevation-dependent stochastic model and Kalman filter estimation model, and the CR denotes the proposed PPP method with a <span class="html-italic">C/N</span><sub>0</sub>-dependent stochastic model and robust Kalman filter estimation model.</p> "> Figure 6 Cont.
<p>Comparison of the static PPP errors with different combinations of GPS, GPS/BDS, and GPS/BDS/GLONASS observed by the Huawei Mate30 in three directions. (<b>a</b>) GPS; (<b>b</b>) three-dimensional deviation points of GPS. (<b>c</b>) GPS/BDS; (<b>d</b>) three-dimensional deviation points of GPS/BDS. (<b>e</b>) GPS/BDS/GLONASS; (<b>f</b>) three-dimensional deviation points of GPS/BDS/GLONASS. The E and N denote east and north direction. The EK denotes the conventional PPP method with an elevation-dependent stochastic model and Kalman filter estimation model, and the CR denotes the proposed PPP method with a <span class="html-italic">C/N</span><sub>0</sub>-dependent stochastic model and robust Kalman filter estimation model.</p> "> Figure 7
<p>Comparison of the RMS of static PPP errors with respect to GPS, GPS/BDS, and GPS/BDS/GLONASS observed by the Huawei Mate30 in 10, 60, and 100 min. The RMS of single frequency and dual frequency PPP errors of the geodetic receiver during the same time period is presented for comparison. GEO denotes geodetic receiver; SF and DF denote single frequency and dual frequency, respectively.</p> "> Figure 8
<p>Comparison of the kinematic PPP errors between the conventional and the proposed models with respect to three combinations of GPS, BDS, and GLONASS.</p> ">
Abstract
:1. Introduction
2. GNSS Signal Characteristics
2.1. Duty Cycle
2.2. Carrier-to-Noise Ratio
2.3. Cycle Slip
2.4. Phase-Code Differences
2.5. Pseudorange Residual
3. Multi-GNSS PPP Mathematical Model
3.1. Uncombined Model
3.2. C/N0-Dependent Stochastic Model
3.3. Parameter Estimation Model Based on Robust Kalman Filter
4. Experiment and Result
4.1. Multi-GNSS Static PPP Solution
4.2. Multi-GNSS Kinematic PPP Solution
5. Conclusions and Remark
- (1)
- The duty cycle can seriously affect the carrier phase observation data logging on some early smartphones, such as the Huawei Mate9, but it has little effect on some new smartphones such as the Huawei Mate30 and Huawei P30.
- (2)
- Unlike the geodetic receiver, the GNSS satellites with high elevation do not necessarily bring the high carrier-to-noise ratio on the smartphone. The rate of GNSS carrier phase cycle slip on the Huawei Mate30 is inversely related to the carrier-to-noise ratio, and the most cycle slips are largely concentrated in the carrier-to-noise ratio below 30 dB-Hz. This means that the conventional stochastic model depending on elevation is difficult to accurately reflect the GNSS observation quality of the smartphone.
- (3)
- In the phase–code differences experiment, the gradual accumulation of phase errors is most marked in BDS on the Huawei Mate30, and the trends correspond to a change of about −1.401 cm/s for BDS. Meanwhile, some extra cycle slips in BDS can be detected by MPF when the difference value between the carrier phase observations (in meters) and the pseudorange observations reached the threshold of 50 m. Moreover, the L5 band of GPS is also affected by the gradual accumulation of phase errors, but the trends are hard to draw.
- (4)
- The comparison results of GNSS pseudorange residuals show that the RMS of GLONASS pseudorange residuals on the Huawei Mate30 is lower than that of GPS and BDS. Moreover, as the C/N0 value increased, the RMS of GPS, BDS, and GLONASS pseudorange residuals all decreased significantly.
Author Contributions
Funding
Conflicts of Interest
References
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C/N0 (dB-Hz) | Pseudorange Residuals RMS (m) | |||
---|---|---|---|---|
GPS | BDS | GLONASS | ||
P1 | P5 | P1 | P1 | |
C/N0 ≤ 30 | 5.388 | 5.730 | 6.964 | 10.604 |
30 < C/N0 ≤ 35 | 4.291 | 4.786 | 3.160 | 8.483 |
35 < C/N0 ≤ 40 | 3.307 | N/A | 2.762 | 7.497 |
40 < C/N0 | 2.774 | N/A | 4.320 | 5.032 |
Static Precise Point Positioning Error RMS (m) | ||||||||
---|---|---|---|---|---|---|---|---|
Direction | Huawei Mate30 | Geodetic Receiver | ||||||
G(EK) | G(CR) | GB(EK) | GB(CR) | GBR(EK) | GBR(CR) | SF | DF | |
10 min | ||||||||
E | 0.520 | 0.298 | 0.207 | 0.221 | 0.095 | 0.273 | 0.341 | 0.129 |
N | 0.701 | 0.594 | 0.221 | 0.533 | 0.614 | 0.387 | 0.567 | 0.114 |
U | 4.645 | 2.352 | 2.954 | 2.081 | 3.432 | 1.302 | 1.119 | 0.580 |
3D | 4.726 | 2.444 | 2.969 | 2.160 | 3.488 | 1.386 | 1.300 | 0.605 |
100 min | ||||||||
E | 0.183 | 0.160 | 0.150 | 0.125 | 0.085 | 0.188 | 0.207 | 0.056 |
N | 0.293 | 0.253 | 0.160 | 0.207 | 0.276 | 0.165 | 0.323 | 0.037 |
U | 1.850 | 0.878 | 1.333 | 0.817 | 1.545 | 0.761 | 0.478 | 0.205 |
3D | 1.882 | 0.928 | 1.351 | 0.852 | 1.572 | 0.801 | 0.613 | 0.216 |
Direction | Kinematic Precise Point Positioning Error RMS (m) | |||||
---|---|---|---|---|---|---|
G(EK) | G(CR) | G + B(EK) | G + B(CR) | G + B + R(EK) | G + B +R(CR) | |
E | 0.962 | 0.763 | 0.866 | 0.897 | 1.257 | 0.928 |
N | 0.857 | 0.707 | 0.754 | 0.640 | 0.778 | 0.624 |
U | 4.162 | 2.235 | 3.621 | 2.062 | 2.722 | 2.167 |
Max | 35.991 | 17.442 | 17.298 | 11.083 | 16.543 | 10.317 |
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Zhu, H.; Xia, L.; Wu, D.; Xia, J.; Li, Q. Study on Multi-GNSS Precise Point Positioning Performance with Adverse Effects of Satellite Signals on Android Smartphone. Sensors 2020, 20, 6447. https://doi.org/10.3390/s20226447
Zhu H, Xia L, Wu D, Xia J, Li Q. Study on Multi-GNSS Precise Point Positioning Performance with Adverse Effects of Satellite Signals on Android Smartphone. Sensors. 2020; 20(22):6447. https://doi.org/10.3390/s20226447
Chicago/Turabian StyleZhu, Hongyu, Linyuan Xia, Dongjin Wu, Jingchao Xia, and Qianxia Li. 2020. "Study on Multi-GNSS Precise Point Positioning Performance with Adverse Effects of Satellite Signals on Android Smartphone" Sensors 20, no. 22: 6447. https://doi.org/10.3390/s20226447