Consistency-Aided Maximum Correntropy Filtering for Multi-Sensor Train Speed Estimation

Reliable train speed estimation requires wheel-speed and Doppler-radar measurements to be fused with acceleration propagation and sparse trackside positioning. Adaptive maximum correntropy filtering can suppress abnormal speed observations. However, because it weights each innovation relative to the predicted state, an accelerometer error that shifts the prediction may cause valid wheel and radar observations to be rejected. This paper proposes a consistency-aided adaptive maximum correntropy robust filter, termed CA-AMCRF, for asynchronous multi-sensor train speed estimation. The filter uses a five-state bias-augmented model to fuse acceleration, wheel speed, Doppler-radar speed, and sparse balise position measurements. During each direct-speed update, a consistency support mechanism compares the bias-compensated wheel–radar disagreement with their common departure from the predicted speed. The resulting evidence corrects the AMCRF measurement weights, recovering reliable direct-speed information during prediction-side errors while preserving robust downweighting when either direct-speed group is abnormal. Controlled simulations show that the method remains robust to isolated wheel- or radar-speed errors and improves accuracy when an accelerometer error shifts the prediction while both direct-speed groups remain reliable. Physics-based adhesion simulations further show the same benefit when an accelerometer error coexists with adhesion-induced wheel-speed distortion.