基于惯性导航的城市轨道交通CBTC无线环境监测系统网格连续定位技术研究
李宏宇1朱俊2王历珘3吴杰3
Grid Continuous Positioning Technology for Urban Rail Transit CBTC Wireless Environment Monitoring System Based on Inertial Navigation
LI Hongyu1ZHU Jun2WANG Lizhou3WU Jie3
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作者信息:1.上海伽易信息技术有限公司，200233，上海
2.上海申通地铁集团有限公司博士后科研工作站,201103，上海
3.上海地铁维护保障有限公司通号分公司，200235，上海
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Affiliation:1.Shanghai Jiayi Information Technology Co., Ltd., 200233, Shanghai, China
2.Postdoctoral Research Workstation, Shanghai Shentong Metro Group Co., Ltd., 201103, Shanghai, China
3.Signal Branch of Shanghai Metro Maintenance and Support Co., Ltd., 200235, Shanghai, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2024.08.050
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中图分类号/CLCN:U231.7
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栏目/Col:应用技术
摘要:
［目的］由于城市轨道交通现有列车信标次级定位系统的定位精度为300m，不能满足无线环境监测网格的高精准定位需求，同时GPS(全球定位系统)和北斗卫星导航系统不能用于地下轨道定位，因此，需对城市轨道交通CBTC(基于通信的列车控制)无线环境监测网格连续定位技术进行深入研究。［方法］在城市轨道交通现有列车信标次级定位系统的基础上，采用信标辅助训练的惯性导航技术，通过多传感器融合定位算法，将惯性导航模块的传感器数据和ATS(列车自动监控)系统的信标数据相融合，实现城市轨道交通无线环境监测网格的连续定位。同时采用回归预测算法进行模型离线训练，用于提高城市轨道交通无线环境监测网格连续定位的准确性。［结果及结论］该系统可以实现无线环境监测网格的连续、实时定位，支持CBTC无线环境监测网格在隧道内部、高架下方，以及途经高楼林立和树木遮挡等多种复杂环境下的定位应用，定位精度达10m，满足CBTC无线环境监测系统的定位需求，全面保障了城市轨道交通CBTC信号系统的安全。
Abstracts:
［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.
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