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Improved Method for Single and Multiple GNSS Faults Exclusion based on Consensus Voting

Published online by Cambridge University Press:  27 February 2019

Qieqie Zhang ,
Long Zhao  and
Jianhua Zhou
Qieqie Zhang
Affiliation:
(Science and Technology on Aircraft Control Laboratory, Beihang University, Beijing 100191, China) (Digital Navigation Center, Beihang University, Beijing 100191, China)
Long Zhao*
Affiliation:
(Science and Technology on Aircraft Control Laboratory, Beihang University, Beijing 100191, China) (Digital Navigation Center, Beihang University, Beijing 100191, China)
Jianhua Zhou
Affiliation:
(Science and Technology on Aircraft Control Laboratory, Beihang University, Beijing 100191, China) (Digital Navigation Center, Beihang University, Beijing 100191, China) (Beijing Satellites Navigation Center, Beijing 100094, China)
*
(E-mail: [email protected])
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Abstract

Receiver Autonomous Integrity Monitoring (RAIM) provides an integrity service for Global Navigation Satellite Systems (GNSS). The conventional RAIM algorithm is based on the assumption of a single fault and typically uses the forward-backward method, which is based on the w-test or correlation analysis methods, to exclude the faults. It is suitable for single fault detection and exclusion, while it can lead to inefficiency, can be misleading and can even fail in the exclusion of multiple faults. To solve this problem, an improved method based on consensus voting of the w-test and correlation analysis methods is presented. To verify the performance of the improved method, tests using Global Positioning System (GPS)/BeiDou System (BDS) data have been carried out in comparison with the conventional methods in terms of false and correct faults exclusion rate and computational complexity in the case of a different number of faults. Results show that the improved method has almost the same correct exclusion rate compared to the conventional RAIM in the case of a single fault. It is worth noting that the improved method has a higher correct exclusion probability and computational efficiency as well as a lower possibility of false exclusion in the case of multiple faults.

Keywords

GNSSRAIMFault detection and exclusionMultiple faultsConsensus voting
Type
Research Article
Information
The Journal of Navigation , Volume 72 , Issue 4 , July 2019 , pp. 987 - 1006
Copyright
Copyright © The Royal Institute of Navigation 2019 

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References

REFERENCES

Baarda, W. (1968). A testing procedure for use in geodetic networks. Netherlands Geodetic Commission, Publication on Geodesy, New Series 2, No. 5, Delft, Netherlands.Google Scholar
Bei, J.Z., Gu, S.Z. and Fang, S.S. (2010). A new RAIM method based on vector correlation distance, Scientia Sinica (Physica, Mechanica & Astronomica), 40, 638–643.Google Scholar
Brown, R.G. (1992). A Baseline GPS RAIM Scheme and a Note on the Equivalence of Three RAIM Methods. Navigation, 39, 301–316. doi:10.1002/j.2161-4296.1992.tb02278.xGoogle Scholar
Brown, R.G. and McBurney, P.W. (1988). Self-contained GPS integrity check using maximum solution separation. Navigation, 35, 41–53. doi:10.1002/j.2161-4296.1988.tb00939.xGoogle Scholar
Cao, K., Hu, Y., Xu, J. and Li, B. (2013) Research on improved RAIM algorithm based on parity vector method. International Conference on Information Technology and Applications, Taiyuan, China, 221–224.10.1109/ITA.2013.58Google Scholar
Chen, J.P., Wang, J., Zhang, Y.Z., Yang, S.N., Chen, Q. and Gong, X.Q. (2016). Modeling and Assessment of GPS/BDS Combined Precise Point Positioning. Sensors, 16, 1151–1163.10.3390/s16071151Google Scholar
DeGroot, M.H and Schervish, M.J. (2011). Probability and Statistics. Pearson Education, Beijing.Google Scholar
Feng, S.J., Ochieng, W.Y., Walsh, D. and Ioannides, R. (2006) A measurement domain receiver autonomous integrity monitoring algorithm, GPS Solutions, 10, 85–96. doi:10.1007/s10291-005-0010-8Google Scholar
Filliben, J.J. (1975). The probability plot correlation coefficient test for normality. Technometrics, 17, 111–117. doi:10.1080/00401706.1975.10489279Google Scholar
Hewiston, S. and Wang, J.L. (2006). GNSS receiver autonomous integrity monitoring (RAIM) performance analysis. GPS Solutions, 10, 155–170. doi:10.1007/s10291-005-0016-2Google Scholar
Hewitson, S., Lee, H.K. and Wang, J.L. (2004). Localizability analysis for GPS/Galileo receiver autonomous integrity monitoring. The Journal of Navigation, 57, 245–259. doi:10.1017/S0373463304002693Google Scholar
Hu, Y.F., Lu, J.J., Cao, K.J. and Li, B. (2014). RAIM algorithm based on correlation distance with several epoch. Journal of Detection & Control, 36, 42–45.Google Scholar
Innac, A., Bhuiyan, M.Z.H., Söderholm, S., Kuusniemi, H. and Gaglione, S. (2016). Reliability testing for multiple GNSS measurement outlier detection. European Navigation Conference, Helsinki, Finland, 530–540. doi:10.1109/EURONAV.2016.7530540Google Scholar
Joerger, M. and Pervan, B. (2014). Solution separation and Chi-Squared ARAIM for fault detection and exclusion. Proc. IEEE/ION PLANS 2014, Institute of Navigation, Monterey, CA, USA, May 5–8, 294–307.10.1109/PLANS.2014.6851388Google Scholar
Kaplan, E.D. and Hegarty, C.J. (2006). Understanding GPS: principles and applications. Artech House, London.Google Scholar
Knight, N.L., Wang, J.L. and Rizos, C. (2009). GNSS integrity monitoring for two satellite faults. IGNSS Symposium 2009, Surfers Paradise, Australia, 221–224.Google Scholar
Knight, N.L., Wang, J.L. and Rizos, C. (2010). Generalised measures of reliability for multiple outliers. Journal of Geodesy, 84, 625–635. doi:10.1007/s00190-010-0392-4Google Scholar
Kuusniemi, H., Wieser, A., Lachapelle, G. and Takala, J. (2007). User-Level Reliability Monitoring in Urban Personal Satellite-Navigation. IEEE Transactions on Aerospace & Electronic Systems, 43, 1305–1318.10.1109/TAES.2007.4441741Google Scholar
Lee, Y.C. (1986). Analysis of range and position comparison methods as a means to provide GPS integrity in the user receiver. Proceedings of ION GPS 1986, Institute of Navigation, Seattle, Washington, June 24–26, 1–4.Google Scholar
McAllister, D.F., Sun, C.E. and Vouk, M.A. (1990). Reliability of voting in fault-tolerant software systems for small output-spaces. IEEE Transactions on Reliability, 39, 524–534. doi:10.1109/24.61308Google Scholar
McBurney, P.W. and Brown, R.G. (1989). Self-contained GPS integrity monitoring using a censored Kalman filter. Proceedings of ION GPS 1988, Institute of Navigation, Colorado Spring, CO, September 19–23, 441–450.Google Scholar
Ni, J., Zhu, Y. and Guo, W. (2007). An improved RAIM scheme for processing multiple outliers in GNSS. International Conference on Advanced Information Networking and Applications Workshops, Niagara Falls, Ont., Canada, 840–845.10.1109/AINAW.2007.86Google Scholar
Pan, Z., Chai, H. and Kong, Y. (2017). Integrating multi-GNSS to improve the performance of precise point positioning. Advances in Space Research, 60, 2596–2606. doi:10.1016/j.asr.2017.01.014Google Scholar
Parkinson, B. and Penina, A. (1988). Autonomous GPS integrity monitoring using the pseudorange residual. Navigation, 35, 255–274. doi:10.1002/j.2161-4296.1988.tb00955.xGoogle Scholar
Ren, D. and Ching-Fang, L. (1995). A new failure detection approach and its application to GPS autonomous integrity monitoring. IEEE Transactions on Aerospace and Electronic Systems, 31, 499–506. doi:10.1109/ 7.366336Google Scholar
Salos, D., Martineau, A., Macabiau, C., Bonhoure, B. and Kubrak, D. (2014). Receiver Autonomous Integrity Monitoring of GNSS signals for electronic toll collection. IEEE Transactions on Intelligent Transportation Systems, 15, 94–103. doi:10.1109/tits.2013.2273829Google Scholar
Sturza, M.A. (1988). Navigation system integrity monitoring using redundant measurements. Navigation, 35, 483–501. doi:10.1002/j.2161-4296.1988.tb00975.xGoogle Scholar
Takasu, T. and Yasuda, A. (2013). RTKLIB ver. 2.4.2 Manual. http://www.rtklib.com/rtklib.htm.Google Scholar
Tang, Y.M., Wang, J. and Peng, X.G. (2011). Research on integrated navigation RAIM model based on correlation analysis. Journal of Geodesy and Geodynamics, 31, 139–143.Google Scholar
Wang, H. (2015). An Improved RAIM algorithm on simultaneous two-faulty satellites. 2015 8th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China, 643–648.10.1109/ISCID.2015.65Google Scholar
Yang, L., Li, Y., Wu, Y. and Rizos, C. (2014). An enhanced MEMS-INS/GNSS integrated system with fault detection and exclusion capability for land vehicle navigation in urban areas. GPS Solutions, 18, 593–603. doi:10.1007/s10291-013-0357-1Google Scholar
Yang, Y. and Xu, J. (2016). GNSS receiver autonomous integrity monitoring (RAIM) algorithm based on robust estimation. Geodesy and Geodynamics, 7, 117–12.10.1016/j.geog.2016.04.004Google Scholar
Yang, L., Wang, J., Knight, N. L. and Shen, Y.Z. (2013). Outlier separability analysis with a multiple alternative hypotheses test. Journal of Geodesy, 87, 591–604. doi:10.1007/s00190-013-0629-0Google Scholar
Yoo, J., Ahn, J., Lee, Y.J. and Sung, S. (2012). Performance Comparison of GPS Fault Detection and isolation via pseudorange prediction model based test statistics. Journal of Electrical Engineering & Technology, 7, 797–806. doi:10.5370/JEET.2012.7.5.797Google Scholar
Zhao, L., Gao, N., Huang, B. and Wang, Q. (2015). A Novel Terrain-Aided Navigation Algorithm Combined With the TERCOM Algorithm and Particle Filter. IEEE Sensors Journal, 15, 1124–1131. doi:10.1109/JSEN. 2014.2360916Google Scholar