零速修正辅助的可见光通信列车连续定位方法  

Continuous Train Positioning Using Visible Light Communication Assisted with Zero Velocity Update

作　　者：张雁鹏[1,2] 张容容 孟楠 张冰清 肖夏 ZHANG Yanpeng;ZHANG Rongrong;MENG Nan;ZHANG Bingqing;XIAO Xia(School of Automation&Electrical Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China;Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphics&Image Processing,Lanzhou 730070,China;China Railway Shenyang Bureau Group Company Limited,Shenyang 110001,China;School of Microelectronics,Tianjin University,Tianjin 300072,China)

机构地区：[1]兰州交通大学自动化与电气工程学院,甘肃兰州730070 [2]甘肃省人工智能与图形图像处理工程研究中心,甘肃兰州730070 [3]中国铁路沈阳局集团有限公司,辽宁沈阳110001 [4]天津大学微电子学院,天津300072

出　　处：《工程科学与技术》2024年第6期248-257,共10页Advanced Engineering Sciences

基　　金：国家自然科学基金项目(62066024);甘肃省高等学校青年博士基金项目(2022QB–068);兰州交通大学–天津大学联合创新基金项目(2021060)。

摘　　要：精确的列车位置信息是基于通信的列车控制(CBTC)系统实现安全行车、间隔控制的基础。现有的可见光通信列车定位相关研究往往关注列车处于静止或匀速运动的情况,较少考虑惯性测量单元(IMU)的累积误差及列车静止后IMU的零速偏移。为了实现隧道环境下地铁列车连续定位,提出一种借助零速修正的可见光通信列车定位方法。根据可见光通信(VLC)原理,安装在列车头部顶端的相机连续接收隧道壁上发光二极管(LED)光源的光信号,即LED光源闪烁的频率信息,获取LED光源对应的位置信息,进而推算列车的实际位置。同时,根据列车运行的速度、加速度等参数,采用零速修正算法,判断列车处于运动或零速状态;通过无迹卡尔曼滤波,对运动区段内列车恒定速度模型和恒定加速度模型下的列车定位结果进行优化,并对列车停站时IMU的零速偏移进行校正,补偿列车接收端位置和姿态估计精度的发散,实现隧道环境下列车高精度连续定位。实验结果表明:运动区段内,列车以40、60和80 km/h的速度匀速直线运行时,最大定位误差分别为15.27、16.93和19.57 cm;以80 km/h为最大目标速度时,匀加速和匀减速运动的最大定位误差分别为18.48和18.24 cm;利用零速修正和卡尔曼滤波优化列车定位结果,列车停站时的平均定位误差由11.35 cm降低至6.05 cm。所提方法有效抑制了IMU的零速偏移,满足IEEE 1474.1—2004标准对列车定位精度的要求,可为面向车车通信的CBTC系统实现列车定位提供一定的参考。Objective Accurate train location information is crucial for communications-based train control(CBTC)systems to ensure safe operation.With the current trend towards vehicle-to-vehicle communication and fully automatic operation (FAO) for subways, CBTC systems demand highly ac-curate and real-time train positioning. Existing research into train positioning using visible light communication (VLC) achieves train location in-formation by calculating the positional relationship between the receiving and sending ends. This attracts attention when a train is stationary or running at a uniform speed. However, little consideration has been paid to the accumulated error from the inertial measurement unit (IMU) and the zero-velocity offset of the IMU. The zero velocity update (ZUPT) algorithm can correct IMU errors by detecting the vehicle’s motion state and at-titude angle to improve the accuracy and stability of positioning. The algorithm is simple, easy to implement, and has many applications in vehicle inertial navigation, robot positioning, human motion recognition, and other fields. This study proposes a continuous train positioning method based on VLC and ZUPT to optimize the results of dynamic train positioning and correct the offset from the IMU when a train is pulled over at a station, enabling seamless train positioning with high accuracy along an entire subway line. The proposed method can improve the accuracy of train positioning in tunnel environments and provide a reference for autonomous train positioning of CBTC systems toward vehicle-to-vehicle communication. Methods This study uses the LED light sources on the tunnel wall as the sending ends of VLC, while the binocular complementary metal-oxide-semiconductor (CMOS) camera fixed on the top carrier platform of the train head serves as the receiving end. This setup enables seamless train positioning with high accuracy along the entire subway line. First, based on the principle of VLC, the binocular CMOS camera at the head of the train captures images of th

关 键 词：可见光通信 基于通信的列车控制 列车定位 零速修正 无迹卡尔曼滤波 

分 类 号：TN929.1[电子电信—通信与信息系统]