Train combination positioning model based on adaptive factor graph under BDS signal loss lock
Aiming at the problem that the train running environment was complex and the BDS signal was easy to lose lock,which affected the accuracy of the BDS/IMU train positioning system,a combined BDS/IMU/OD train positioning model based on an adaptive factor graph was proposed.On the basis of the BDS/IMU train positioning system,the Odometer(OD)positioning technology was introduced.The Beidou Navigation Satellite System(BDS)and the Inertial Measurement Unit(IMU)and OD three types were used.The sensor obtained the train measurement information,according to the factor graph theory,described the multi-source measurement information as a state space equation,abstracted BDS,IMU,OD factor nodes and prior factors,and determined the undirected connection relationship between factor nodes and variable nodes.It established a multivariate train combination location factor graph model,and calculated the location information of the train.When the BDS signal undergoes changed,an adaptive factor algorithm was proposed using the plug and play feature of the factor graph to dynamically adjust the structure of the train combination positioning factor graph model.When the BDS information was partially unlocked,a BDS/IMU/OD train combination positioning factor graph model was established using the partial information of BDS.When the BDS information was completely unlocked,it was converted into an IMU/OD train combination positioning factor graph model to suppress the divergence error caused by the complete unlocking of BDS.Using the Kalman algorithm,factor graph algorithm,and adaptive factor graph algorithm,the simulation analysis of train positioning is carried out.When the BDS information is partially out of lock,the root mean square error of the positioning position of the adaptive factor graph model will be reduced separately 52.3%,48.2%and 42.7%than that of the Kalman algorithm which are 34.8%,27.0%and 25.2%lower than the factor graph algorithm.When the BDS information is completely out of lock,the positioning position error of the adaptive factor graph model is 46.7%,46.7%and 50%lower than that of the Kalman algorithm,respectively.The adaptive factor graph algorithm can improve the train positioning accuracy when the BDS information is out of lock,and it can realize plug-and-play between different sensors.The system provides model support.
train combination positioningBDS signal loss lockfactor graphBDS/IMU/ODadaptive factor
万方数据
维普
