State Estimation for High-Speed Train Based on Multirate Asynchronous Sensors Fusion With Missing Measurements

Abstract

In this paper, a state estimation method is proposed based on the multi-rate asynchronous sensors fusion with missing measurements for high-speed train. Firstly, considering the actual operating environment, a multi-rate asynchronous speed measurement state space model is established. Then, a maximum likelihood evaluation criterion is constructed, and the multiple imputation fusion is proposed for the recovery of intermittent and continuous missing data. Finally, based on the improved adaptive attenuation particle filter-unscented Kalman filter (PF-UKF) and the matrix weighted fusion, the global estimation is fused by linear-weighted summation of the local estimations of train state. The simulation results show that the train state can be accurately fused and estimated by the proposed method while ensuring real-time performance, whether the monitoring data is intermittent missing or continuous missing, and the effectiveness and feasibility of the method can be verified.