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

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.