Research On Train Multi-Source Location Information Fusion Method Based On BDS/SINS/DR

The train positioning based on Beidou satellite navigation(BDS)and strapdown inertial navigation system(SINS)can provide accurate and reliable positioning information for the train operation control system.Kalman filtering algorithm can correct the cumulative error of SINS based on the observation information of navigation satellites.However,in the process of train operation,the positioning observation has time-varying characteristics,and the conventional Kalman filtering algorithm cannot adapt to the dynamic changes,resulting in the problem of positioning performance degradation or filter divergence.In addition,the observation quality of satellite signal is easily affected by the train operation environment,thereby reducing the BDS/SINS integrated positioning performance.Solving the problem of insufficient positioning accuracy of train under vehicle-mounted satellite lockout has become a key factor in the application of Beidou satellite navigation system for train control.In order to improve the train positioning performance,the BDS/SINS/DR train positioning framework is constructed,and the sequential auxiliary adaptive fading unscented Kalman filter(UKF)algorithm is designed.The proposed train positioning method is verified according to the degree of satellite lockout.The specific work and innovations are as follows:(1)Aiming at the problem of rapid change or even mutation of positioning observation in train operation,which affects the positioning accuracy of trains,a train positioning information fusion algorithm based on sequential auxiliary adaptive fading derivative UKF is proposed.The algorithm is based on the characteristics of BDS/SINS tightly coupled train positioning information fusion model.Firstly,a derivative Kalman filter algorithm is used to ensure the filtering accuracy and effectively reduce the difficulty of filtering calculation.Secondly,this algorithm introduces an adaptive multiple fading factor based on the derivative Kalman filtering algorithm,adjusts the filtering gain in real time,reduces the influence of noise in the measurement equation on the state update,and improves the filtering accuracy.Finally,the sequential auxiliary method is used to increase the flexibility of multiple fading factors,so that different filtering channels have different adjustment capabilities and improve the overall performance of the algorithm.The performance of the improved UKF algorithm is analyzed by off-line simulation of the measured train positioning data.The results show that the train positioning accuracy of the improved UKF algorithm is significantly better than that of EKF and UKF algorithm.(2)In view of the problem that the vehicle-borne Beidou satellite signal is completely locked due to the environmental impact of the train,which leads to the failure of the train positioning accuracy to meet the requirements of the train positioning,this paper introduces the dead reckoning(DR)technology,adopts a SINS/DR train positioning model,and proposes a sequential KF algorithm based on adaptive single fading suitable for the SINS/DR train positioning model.On this basis,the BDS/SINS/DR train integrated positioning method is designed to improve the positioning performance of trains in the case of lock loss.The off-line simulation based on the measured data verifies the designed model.The results show that when the train satellite signal is completely locked,the SINS/DR train positioning model can provide navigation accuracy below 10 m in 50 s,which meets the basic requirements of train positioning.