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Enhancing train position perception through AI-driven multi-source information fusion

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Abstract

This paper addresses the challenge of accurately and timely determining the position of a train, with specific consideration given to the integration of the global navigation satellite system (GNSS) and inertial navigation system (INS). To overcome the increasing errors in the INS during interruptions in GNSS signals, as well as the uncertainty associated with process and measurement noise, a deep learning-based method for train positioning is proposed. This method combines convolutional neural networks (CNN), long short-term memory (LSTM), and the invariant extended Kalman filter (IEKF) to enhance the perception of train positions. It effectively handles GNSS signal interruptions and mitigates the impact of noise. Experimental evaluation and comparisons with existing approaches are provided to illustrate the effectiveness and robustness of the proposed method.

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Author information

Authors and Affiliations

  1. Electronic Information Engineering, Beihang University, Beijing, 100191, China

    Haifeng Song

  2. State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing, 100044, China

    Zheyu Sun, Zixuan Zhang & Hairong Dong

  3. National Research Center of Railway Safety Assessment, Beijing Jiaotong University, Beijing, 100044, China

    Hongwei Wang

  4. Dalian Locomotive and Rolling Stock Co., Ltd., CRRC Corporation Limited, Dalian, 116022, Liaoning, China

    Tianwei Qu

Authors
  1. Haifeng Song
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  2. Zheyu Sun
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  3. Hongwei Wang
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  4. Tianwei Qu
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  5. Zixuan Zhang
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  6. Hairong Dong
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Corresponding author

Correspondence to Haifeng Song.

Additional information

This work was supported by the National Natural Science Foundation of China (Nos. 61925302, 62273027) and the Beijing Natural Science Foundation (L211021).

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Song, H., Sun, Z., Wang, H. et al. Enhancing train position perception through AI-driven multi-source information fusion. Control Theory Technol. 21, 425–436 (2023). https://doi.org/10.1007/s11768-023-00158-7

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  • DOI: https://doi.org/10.1007/s11768-023-00158-7

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