Enhancing train position perception through Al-driven multi-source information fusion
Haifeng Song1
Zheyu Sun2
Hongwei Wang3
Tianwei Qu4
Zixuan Zhang2
Hairong Dong2
1.Electronic Information Engineering,Beihang University,Beijing 100191,China2.State Key Laboratory of Rail Traffic Control and Safety,Beijing Jiaotong University,Beijing 100044,China3.National Research Center of Railway Safety Assessment,Beijing Jiaotong University,Beijing 100044,China4.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.
机标关键词：informationfusionthroughal-drivenenhancingmulti-sourceperceptionposition
论文发表日期：2023-08-05
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：12( 425-436 )
英文信息