Fusion Speed Measurement Algorithm of High-speed Train Based on Robust Adaptive Filter

Abstract:

A fusion speed measurement algorithm of high-speed trains based on robust adaptive filter was proposed to solve the problem that the fusion accuracy decreased due to the observation gross errors and the dynamic model errors in the fusion speed measurement using Kalman filter. Firstly， the anomaly detection function and error discrimination statistics were constructed on the basis of Kalman filter， which were used to detect and distinguish the observation gross errors and dynamic model errors caused by abnormal observations of sensors. Then， for observation gross errors and dynamic model errors， a three-segment function and an exponential function were used to construct robust factor and adaptive factor， respectively. The weights of observation information and model information in state estimation were reasonably adjusted by the two factors， so as to reduce the impact of observation gross errors and dynamic model errors on the fusion results. Finally， the performance of robust adaptive filter was verified by simulation with two operation scenes and comparison between algorithms. The simulation results show that compared with the fusion speed measurement algorithm based on Kalman filter， the proposed algorithm has higher accuracy and stability in both the observation gross errors scene and the dynamic model errors scene.