Grid Continuous Positioning Technology for Urban Rail Transit CBTC Wireless Environment Monitoring System Based on Inertial Navigation

Objective The current secondary positioning system of urban rail transit train beacons has a positioning accuracy of 300 meters, failing to meet the high-precision positioning requirements of wireless environment monitoring grid. Additionally, GPS (global positioning system) and the Beidou Satellite Navigation System are not suitable for underground rail positioning. Therefore, it is necessary to conduct in-depth research on continuous positioning technology for urban rail transit CBTC (communication-based train control) wireless environment monitoring grid. Method Building on the existing urban rail transit train beacon secondary positioning system, an inertial navigation technology enhanced by beacon-assisted training is adopted. By utilizing a multi-sensor fusion positioning algorithm, the sensor data from the inertial navigation module is integrated with the beacon data from the ATS (automatic train supervision) system, thus achieving the continuous positioning of wireless environment monitoring grid in urban rail transit. Additionally, a regression prediction algorithm is used for offline model training to enhance the accuracy of continuous positioning for the urban rail transit wireless environment monitoring grid. Result & Conclusion The proposed methods can achieve continuous and real-time positioning of wireless environment monitoring grid, supporting CBTC wireless environment monitoring grid in tunnels, beneath elevated structures, and in complex environments where metro train passing through areas with tall buildings and tree cover. The positioning accuracy reaches 10 m, meeting the positioning requirements of the CBTC wireless environment monitoring system, and fully ensuring the safety of the urban rail transit CBTC signaling system.