For calibrating the measurement error of the speed sensor and estimating the speed accurately while system is nonlinear and noise is non-Gaussian, dual radar and wheel axle sensor are configured and federal particle filter is used to acquire fusion. The whole operation process of CRH3 train from start to stop between two stations is taken as an example, the speed measurement errors of different speed estimation methods under the conditions of train idling, slipping, vibration and the speed sensor with dynamic noise are analyzed and verified. The simulation results show that: The root-mean-square error can be reduced by 31.52% and 47.35% respectively in the idling and sliding stages after the calibration of dual-radar and wheel-axle combination compared with no calibration; Compared with the two types of dual-radar calibration methods, the maximum relative error of the dual-radar separation vibration speed calibration method can be 39.66% lower than that of the dual-radar angle deviation estimation calibration method when the train vibration speed ratio is 0 to 1 and the radar installation angle error is -1° to 1°. Compared with the filtering results of dual radar and axle sensors before fusion, the speed measurement results after using joint particle filtering fusion are 34.71% and 14.03% lower in MAE, and 26.51% and 10.98% lower in RMSE respectively. Compared with federated extended Kalman filter fusion, the root mean square error of velocity measurement using federated particle filter fusion is 26.97% lower and the maximum absolute error can be reduced by 16.10%. [ABSTRACT FROM AUTHOR]