Slip and Slide Detection and Adaptive Information Sharing Algorithms for High-Speed Train Navigation Systems


The position and velocity info of high-speed trains (HSTs) are essential to passenger safety, operational potency, and maintenance, for which an accurate navigation system is required. In this paper, we tend to propose a 2-stage federated Kalman filter (TS-FKF) for an HST navigation system that uses multi-sensors, like tachometer, inertial navigation system, differential GPS, and RFID, with a feedback theme. However, the FKF with a feedback scheme usually shows severe performance degradation within the presence of undetected giant sensor errors. Tachometers typically have large slip or slide errors during the train's acceleration, deceleration, and moving along a curved railway, and there are important performance differences between totally different sensors. To make the proposed system robust to these errors, we propose a mistake and slide detection algorithm for the tachometer and an adaptive information-sharing algorithm to accommodate a massive tachometer error and performance difference between sensors. We tend to give theoretical analysis and simulation results to demonstrate the performance of the proposed navigation system with the proposed algorithms.

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