基于当前统计模型的地图匹配车辆跟踪研究

Study of Map Matching in Vehicle Tracking System Based on the Current Statistical Model

为提高车辆跟踪系统的精度,减小差分全球定位系统(DGPS)的定位误差,通过分析行驶在城市道路上的车辆运动过程及其相应的运动模型,提出采用当前统计模型作为车辆运动模型。通过地图辅助位置择近和速度择角算法来修正卡尔曼滤波,为运行在道路上的车辆确定地图匹配估计。实际运行结果表明:整个车辆跟踪系统的精度有明显的提高。

In order to improve precision of vehicle tracking system and reduce the position error of DGPS, authors first analyzed the motive process of vehicle on the road and its moving model. Then, the current statistical model was proposed as moving model of vehicles because in a city environment, vehicles were restricted to travel on its road. Authors proposed a map-aided algorithm of position nearly selected and velocity angularly selected to modify Kalman filtering. The result shows that the accuracy of the whole vehicle tracking system has obviously been increased.