Track curvature detection based on bogie attitude trajectory information fusion

A curvature detection method based on bogie attitude trajectory information fusion is proposed to improve the accuracy and real-time performance of track curvature detection. The running speed, yaw angular velocity, and roll angle of a bogie were measured to obtain horizontal running and roll attitude trajectories through curve fitting. Track superelevation and curvature were estimated from these trajectories, and the characteristics and mechanisms of the parameter estimation errors were analysed. Data fusion algorithms based on feature point discrimination and superelevation trend tracking were established; these were used to fuse the estimation results of superelevation and curvature from different principles to obtain curvature detection values with higher accuracies. The performance of this curvature detection method was verified through simulations comprehensively. The simulation results indicated that the proposed method effectively improved the accuracy of real-time curvature detection. The correction of the two data fusion algorithms significantly ameliorated the signal lag problem in the traditional low-pass filtering method, reducing the relative error rate to less than 50% of that in the traditional method (maximum reduction of 75%–80%). This study is expected to guide the application and development of railway vehicle active control technologies that use track curvature as a control parameter.