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Non-Line-of-Sight Multipath Detection Method for BDS/GPS Fusion System Based on Deep Learning

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Abstract

Non-line-of-sight (NLOS) multipath effect is the main factor that restricts the application of global navigation satellite system (GNSS) in complex environments, especially in urban canyon. The effective avoidance of NLOS signals can significantly improve the positioning performance of GNSS receiver. In this paper, an NLOS/LOS classification model based on recurrent neural network is proposed to classify satellite signals received in urban canyon environments. The accuracy of classification is 91%, and the recognition rate of NLOS is 89%; the classification performance is better than traditional machine learning classification models such as support vector machine. For BeiDou navigation satellite system/global positioning system (BDS/GPS) fusion system, the least square algorithm and extended Kalman filter are used to estimate the position. The experimental results show that the three-dimensional positioning accuracy after NLOS recognition is improved about 60% on average compared with the traditional methods, and the positioning stability is also improved significantly.

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References

  1. JIANG Z, GROVES P D. NLOS GPS signal detection using a dual-polarisation antenna [J]. GPS Solutions, 2014, 18(1): 15–26.

    Article  Google Scholar 

  2. BÉTAILLE D, PEYRET F, VOYER M, et al. Applying standard digital map data in map-aided, lane-level GNSS location [J]. Journal of Navigation, 2015, 68(5): 827–847.

    Article  Google Scholar 

  3. HSU L T, GU Y, KAMIJO S. 3D building model-based pedestrian positioning method using GPS/GLONASS/QZSS and its reliability calculation [J]. GPS Solutions, 2016, 20(3): 413–428.

    Article  Google Scholar 

  4. GU Y, HSU L T, KAMIJO S. GNSS/onboard inertial sensor integration with the aid of 3-D building map for lane-level vehicle self-localization in urban canyon [J]. IEEE Transactions on Vehicular Technology, 2016, 65(6): 4274–4287.

    Article  Google Scholar 

  5. BAUER S, OBST M, STREITER R, et al. Evaluation of shadow maps for non-line-of-sight detection in urban GNSS vehicle localization with VANETs: The GAIN approach [C]//2013 IEEE 77th Vehicular Technology Conference. Dresden, Germany: IEEE, 2013: 1–5.

    Google Scholar 

  6. GROVES P D. Shadow matching: A new GNSS positioning technique for urban canyons [J]. The Journal of Navigation, 2011, 64(3): 417–430.

    Article  Google Scholar 

  7. WANG L, GROVES P D, ZIEBART M K. Smart-phone shadow matching for better cross-street GNSS positioning in urban environments [J]. Journal of Navigation, 2015, 68(3): 411–433.

    Article  Google Scholar 

  8. YOZEVITCH R, MOSHE B B, LEVY H. Breaking the 1 meter accuracy bound in commercial GNSS devices [C]//2012 IEEE 27th Convention of the Electrical and Electronics Engineers in Israel. Eilat, Israel: IEEE, 2012: 1–5.

    Google Scholar 

  9. SUZUKI T. Particle filter-based GNSS positioning with NLOS multipath detection [C]//31st International Technical Meeting of the Satellite Division of the Institute of Navigation. Miami, USA: ION, 2018: 24–28.

    Google Scholar 

  10. SUZUKI T, NAKANO Y, AMANO Y. NLOS multi-path detection by using machine learning in urban environments [C]//30th International Technical Meeting of the Satellite Division of the Institute of Navigation. Portland, USA: ION, 2017: 25–29.

    Google Scholar 

  11. MUNIN E, BLAIS A, COUELLAN N. Convolutional neural network for multipath detection in GNSS receivers [C]//2020 International Conference on Artificial Intelligence and Data Analytics for Air Transportation. Singapore: IEEE, 2020: 1–10.

    Google Scholar 

  12. SOCHAROENTUM M, KARIMI H A, DENG Y. A machine learning approach to detect non-line-of-sight GNSS signals in Nav2Nav [C]//23rd ITS World Congress. Melbourne, Australia: ITS, 2014: 10–14.

    Google Scholar 

  13. YOZEVITCH R, MOSHE B B, WEISSMAN A. A robust GNSS LOS/NLOS signal classifier [J]. Navigation, 2016, 63(4): 429–442.

    Article  Google Scholar 

  14. HSU L T. GNSS multipath detection using a machine learning approach [C]// 2017 IEEE 20th International Conference on Intelligent Transportation Systems. Yokohama, Japan: IEEE, 2017: 1–6.

    Google Scholar 

  15. COMANICIU D, MEER P. Mean shift: A robust approach toward feature space analysis [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(5): 603–619.

    Article  Google Scholar 

  16. SCARAMUZZA D, MARTINELLI A, SIEGWART R. A toolbox for easily calibrating omnidirectional cameras [C]//2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. Beijing: IEEE, 2006: 5695–5701.

    Google Scholar 

  17. MAO X, WADA M, HASHIMOTO H. A nonlinear model for GPS positioning estimation using filter algorithms [J]. Journal of Shanghai Jiaotong University, 2004, 38(4): 610–615 (in Chinese).

    Google Scholar 

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Authors and Affiliations

  1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Hong Su  (苏 红) & Xuchu Mao  (茅旭初)

  2. Xi’an Satellite Control Center, Xi’an, 710043, China

    Bozhao Wu  (吴博钊)

Authors
  1. Hong Su  (苏 红)
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  2. Bozhao Wu  (吴博钊)
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  3. Xuchu Mao  (茅旭初)
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Correspondence to Xuchu Mao  (茅旭初).

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Su, H., Wu, B. & Mao, X. Non-Line-of-Sight Multipath Detection Method for BDS/GPS Fusion System Based on Deep Learning. J. Shanghai Jiaotong Univ. (Sci.) 27, 844–854 (2022). https://doi.org/10.1007/s12204-022-2430-9

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  • DOI: https://doi.org/10.1007/s12204-022-2430-9

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