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Haifeng Song1,Zheyu Sun2,Hongwei Wang3,et al.[en_title][J].Control Theory and Technology,2023,21(3):425~436.[Copy]
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Enhancing train position perception through AI-drivenmulti-source information fusion
HaifengSong1,ZheyuSun2,HongweiWang3,TianweiQu4,ZixuanZhang2,HairongDong2
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(1 Electronic Information Engineering, Beihang University, Beijing 100191, China;2 State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China;3 National Research Center of Railway Safety Assessment, Beijing Jiaotong University, Beijing 100044, China;4 Dalian Locomotive and Rolling Stock Co., Ltd., CRRC Corporation Limited, Dalian 116022, Liaoning, China)
摘要:
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.
关键词:  Train positioning · Deep learning · Multi-source information fusion · Dynamic adaptive model
DOI:https://doi.org/10.1007/s11768-023-00158-7
基金项目:This work was supported by the National Natural Science Foundation of China (Nos. 61925302, 62273027) and the Beijing Natural Science Foundation (L211021).
Enhancing train position perception through AI-drivenmulti-sourceinformation fusion
Haifeng Song1,Zheyu Sun2,Hongwei Wang3,Tianwei Qu4,Zixuan Zhang2,Hairong Dong2
(1 Electronic Information Engineering, Beihang University, Beijing 100191, China;2 State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China;3 National Research Center of Railway Safety Assessment, Beijing Jiaotong University, Beijing 100044, China;4 Dalian Locomotive and Rolling Stock Co., Ltd., CRRC Corporation Limited, Dalian 116022, Liaoning, China)
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.
Key words:  Train positioning · Deep learning · Multi-source information fusion · Dynamic adaptive model