The measurement accuracy of speed and distance in high speed train directly affects the control precision and driving efficiency of train control system. To improve the capability of train self control, a combined spe...The measurement accuracy of speed and distance in high speed train directly affects the control precision and driving efficiency of train control system. To improve the capability of train self control, a combined speed measurement and positioning method based on speed sensor and radar which is assisted by global positioning system（GPS） is proposed to improve the accuracy of measurement and reduce the dependence on the ground equipment. In consideration of the fact that the filtering precision of Kalman filter will decrease when the statistical characteristics are changing, this paper uses fuzzy comprehensive evaluation method to evaluate the sub filter, and information distribution coefficients are dynamically adjusted according to filtering reliability, which can improve the fusion accuracy and fault tolerance of the system. The sub filter is required to carry on the covariance shaping adaptive filtering when it is in the suboptimal state. The adjustment factor of error covariance is obtained according to the minimized cost function, which can improve the matching degree between the measured residual variance and the system recursive residual. The simulation results show that the improved filter algorithm can track the changes of the system effectively, enhance the filtering accuracy significantly, and improve the measurement accuracies of train speed and distance.展开更多
基金National Natural Science Foundation of China(Nos.61763023,61164010)
文摘The measurement accuracy of speed and distance in high speed train directly affects the control precision and driving efficiency of train control system. To improve the capability of train self control, a combined speed measurement and positioning method based on speed sensor and radar which is assisted by global positioning system（GPS） is proposed to improve the accuracy of measurement and reduce the dependence on the ground equipment. In consideration of the fact that the filtering precision of Kalman filter will decrease when the statistical characteristics are changing, this paper uses fuzzy comprehensive evaluation method to evaluate the sub filter, and information distribution coefficients are dynamically adjusted according to filtering reliability, which can improve the fusion accuracy and fault tolerance of the system. The sub filter is required to carry on the covariance shaping adaptive filtering when it is in the suboptimal state. The adjustment factor of error covariance is obtained according to the minimized cost function, which can improve the matching degree between the measured residual variance and the system recursive residual. The simulation results show that the improved filter algorithm can track the changes of the system effectively, enhance the filtering accuracy significantly, and improve the measurement accuracies of train speed and distance.
