Machine learning based LOS/NLOS classifier and robust estimator for GNSS shadow matching
Tóm tắt
Global Navigation Satellites Systems (GNSS) is frequently used for positioning services in various applications, e.g., pedestrian and vehicular navigation. However, it is well-known that GNSS positioning performs unreliably in urban environments. GNSS shadow matching is a method of improving accuracy in the cross-street direction. Initial position and classification of observed satellite visibility between line-of-sight (LOS) and non-line-of-sight (NLOS) are essential for its performance. For the conventional LOS/NLOS classification, the classifiers are based on a single feature, extracted from raw GNSS measurements, such as signal noise ratio, pseudorange, elevation angle, etc. Especially in urban canyons, these measurements are unstable and unreliable due to the signal reflection and refraction from the surrounding buildings. Besides, the conventional least square approach for positioning is insufficient to provide accurate initialization for shadow matching in urban areas. In our study, shadow matching is improved using the initial position from robust estimator and the satellite visibility determined by support vector machine (SVM). The robust estimator has an improved positioning accuracy and the classification rate of SVM classification can reach 91.5% in urban scenarios. An important issue is related to satellites with ultra-high or low elevation angles and satellites near the building boundary that are very likely to be misclassified. By solving this problem, the SVM classification shows the potential of about 90% classification accuracy for various urban cases. With the help of these approaches, the shadow matching has a mean error of 10.27 m with 1.44 m in the cross-street direction; these performances are suitable for urban positioning.
Tài liệu tham khảo
Adjrad, M., & Groves, P. D. (2017). Enhancing least squares GNSS positioning with 3D mapping without accurate prior knowledge. Journal of The Institute of Navigation,64(1), 75–91. https://doi.org/10.1002/navi.178.
Adjrad, M., & Groves, P. D. (2018). Intelligent urban positioning: integration of shadow matching with 3D-mapping-aided GNSS ranging. Journal of Navigation,71(1), 1–20. https://doi.org/10.1017/S0373463317000509.
Angrisano, A., Del Pizzo, S., Gaglione, S., Troisi, S., & Vultaggio, M. (2018). Using local redundancy to improve GNSS absolute positioning in harsh scenario. Acta Imeko,2018(7), 16–23.
Brown, R. G. (1993). Global positioning system: Theory and applications. In Spilker Jr, J. J., Axelrad , P., Parkinson, B. W., & Enge, P. (Ed.), Gps navigation data (pp. 143–165). The American Institute of Aeronautics and Astronautics. https://doi.org/10.2514/4.866388.
Castaldo, G., Angrisano, A., Gaglione, S., & Troisi, S. (2014). P-RANSAC: An integrity monitoring approach for GNSS signal degraded scenario. International Journal of Navigation and Observation,2014, 1–11. https://doi.org/10.1155/2014/173818.
El-Sheimy, N., & Youssef, A. (2020). Inertial sensors technologies for navigation applications: State of the art and future trends. Satellite Navigation,1, 2. https://doi.org/10.1186/s43020-019-0001-5.
Gaglione, S., Innac, A., Pastore Carbone, S., Troisi, S., & Angrisano, A. (2017). Robust estimation methods applied to GPS in harsh environments. 2017 European Navigation Conference (ENC). Lausanne,2017, 14–25. https://doi.org/10.1109/EURONAV.2017.7954169.
Groves, P. D. (2011). Shadow matching: a new GNSS positioning technique for urban canyons. Journal of Navigation,64(3), 417–430.
Groves, P. D., Wang, L., Adjrad, M., & Ellul, C. (2015). GNSS shadow matching: The challenges ahead. In Proceedings of the ION GNSS+ 2015, 14–18 September 2015, Tampa, Florida, The Institute of Navigation.
Hsu, L. (2017). GNSS multipath detection using a machine learning approach. In: IEEE 20th international conference on intelligent transportation systems (ITSC) 2017 (pp. 1–6), Yokohamaz. doi:10.1109/ITSC.2017.8317700.
Hsu, L., Tokura, H., Kubo, N., Gu, Y., & Kamijo, S. (2017). Multiple faulty GNSS measurement exclusion based on consistency check in urban canyons. IEEE Sensors Journal,17(6), 1909–1917. https://doi.org/10.1109/JSEN.2017.2654359.
Ji, S., Chen, W., Ding, X., Chen, Y., Zhao, C., & Hu, C. (2010). Potential benefits of GPS/GLONASS/GALILEO integration in an urban Canyon—Hong Kong. Journal of Navigation,63(4), 681–693. https://doi.org/10.1017/S0373463310000081.
Knight, N. L., & Wang, J. (2009). A comparison of outlier detection procedures and robust estimation methods in GPS positioning. Journal of Navigation,62(4), 699–709. https://doi.org/10.1017/S0373463309990142.
Kuusniemi, H., Lachapelle, G., & Takala, J. H. (2004). Position and velocity reliability testing in degraded GPS signal environments. GPS Solutions,8(4), 226–237. https://doi.org/10.1007/s10291-004-0113-7.
Rousseeuw, P. J., & Leroy, A. M. (1987). Robust regression and outlier detection. New York: Wiley.
Tay, S., & Marais, J. (2013). Weighting models for GPS Pseudorange observations for land transportation in urban canyons. In: Proceedings of the 6th European workshop on GNSS signals and signal processing, Munich, Germany, pp. 1–6.
Tsakiri, M., Stewart, M., Forward, T., Sandison, D., & Walker, J. (1998). Urban fleet monitoring with GPS and GLONASS. Journal of Navigation,51(3), 382–393. https://doi.org/10.1017/S0373463398007929.
Viandier, N., Nahimana, D. F., Marais, J., & Duflos, E. (2008). Gnss per-formance enhancement in urban environment based on pseudo-rangeerror model. In: Proceedings of IEEE/ION position, location navigation symposium, Monterey, CA, pp. 377–382. doi: 10.1109/PLANS.2008.4570093.
Wang, L. (2014). Kinematic GNSS shadow matching using a particle filter. In: Proceedings of the 27th international technical meeting of the satellite division of the institute of navigation (ION GNSS+ 2014), Tampa, Florida, pp. 1907–1919.
Wang, L., Groves, P. D., & Ziebart, M. K. (2015). Smartphone shadow matching for better cross-street GNSS positioning in urban environments. Journal of Navigation,68(3), 411–433. https://doi.org/10.1017/S0373463314000836.
Xu, H., Zhang, G., Xu, B & Hsu, L. T. (2018). GNSS shadow matching based on intelligent LOS/NLOS Classifier. Paper presented at the The 16th world congress of the international association of institutes of navigation (IAIN) Chiba, Japan.
Yozevitch, R., & Moshe, B. B. (2015). A robust shadow matching algorithm for GNSS positioning. Journal of Navigation,62(2), 95–109. https://doi.org/10.1002/navi.85.
Yang, Y.X., Mao, Y., & Sun, B.J. (2020). Basic performance and future developments of BeiDou global navigation satellite system. Satellite Navigation,1, 1. https://doi.org/10.1186/s43020-019-0006-0.
Yozevitch, R., Moshe, B. B., & Weissman, A. (2016). A robust GNSS LOS/NLOS signal classifier. Journal of Navigation,63(4), 429–442. https://doi.org/10.1002/navi.166.