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Expectation Maximization Algorithm for GPS Positioning in Multipath Environments Based on Volterra Series

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Abstract

The multipath effect error (MEE) is typically not taken into account by the RTKLIB localization method, and this may lead to poor positioning accuracy. This paper proposes an expectation maximization (EM) algorithm for GPS positioning based on Volterra series, and the pseudoranges contaminated by MEE are considered as missing data. Firstly, the Volterra series is introduced to linearize the pseudorange equation. Then, the EM algorithm is used to iteratively update the user location and missing data. Compared with the RTKLIB method, the proposed algorithm has more accurate positioning accuracy. The simulation example shows the effectiveness of the proposed algorithm.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61973137, 62073082), the Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX22_2417) and the Natural Science Foundation of Jiangsu Province (No. BK20201339).

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Authors and Affiliations

  1. School of Science, Jiangnan University, Wuxi, 214122, Jiangsu, People’s Republic of China

    Lianyuan Cheng, Jing Chen, Yawen Mao & Cuicui Liao

  2. Department of Engineering Design and Mathematics, University of the West of England, Bristol, BS161QY, UK

    Quanmin Zhu

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  1. Lianyuan Cheng
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Correspondence to Jing Chen.

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Cheng, L., Chen, J., Mao, Y. et al. Expectation Maximization Algorithm for GPS Positioning in Multipath Environments Based on Volterra Series. Circuits Syst Signal Process 42, 6278–6295 (2023). https://doi.org/10.1007/s00034-023-02407-1

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