基于线路数据信息的列车定位方法研究

Train Location Method Based on Line Data Information

摘要:

针对列车丢失卫星信号无法定位的问题，提出了一种基于动态时间规整（DTW）算法的列车平顺度信息匹配的方法。借助传感器对列车的在线的平顺度信息进行采集，利用基于DTW-KF（卡尔曼滤波）的方法对采集到的数据进行处理与规整匹配。基于列车自身速度传感器等的数据作为模板变量与在线测量数据作为测试数据进行匹配，根据DTW的最优匹配路径、失真度和匹配结果可以在列车在丢失卫星信号，无法定位的情况下给出列车离线定位信息。对模板数据进行分块处理（二均分，四均分）与测试数据进行实时匹配，根据DTW算法匹配结果对惯性导航系统（INS）进行误差校正可以使列车在丢失卫星信号的情况下得到列车的在线定位信息。

Abstract:

Aimed at the problem that the train loses satellite signal and can not be located， a dynamic time warping （DTW） based train smoothness information matching algorithm is proposed. With the help of specific sensor， the online regularity information of the train is collected. Afterward， the collected data is processed and matched by applying DTW-KF （Kalman filter）. Using the data measured by the onboard speed sensor as template variables， the online regularity measurement data is applied to do the matching. According to the optimal matching path， distortion， and matching results of DTW， the train can be positioned offline when the train cannot be located due to the loss of the satellite signals. The template data is divided into blocks （even division and quartile） to match in real-time， and the error correction of the INS （inertial navigation system） based on the matching result of the DTW algorithm can locate the train when the satellite signal loses online location information.