为提高车载毫米波雷达多目标跟踪精度指标,提升道路车辆行驶安全性，文中在交互多模型无迹卡尔曼滤波(IMMUKF)和联合概率数据关联(JPDA)融合的算法基础上，针对车辆运动状态突变处UKF鲁棒性差、滤波精度低的问题，提出了一种基于改进强跟踪UKF(ISTUKF)的IMM-JPDA-ISTUKF 算法。通过模拟道路场景搭建的仿真环境对算法性能进行了验证，且为证明该算法在实际道路工况下跟踪精度的提升，还进行了雷达道路测试，通过雷达在道路上获取的车辆数据进一步验证了该算法的有效性。结果表明,该算法在目标车辆运动状态发生变化时的距离跟踪精度和速度跟踪精度方面均得到了提高。

In order to improve the multi-target tracking accuracy index of vehicle-mounted millimeter-wave radar and enhance the driving safety of road vehicles, based on the fusion of interactive multi-model unscented Kalman filter (IMM-UKF) and joint probabilistic data association (JPDA), according to improved strong tracking UKF (ISTUKF) an IMM-JPDA-ISTUKF algorithm is proposed in this paper to solve the problems of poor robustness and low filtering accuracy of UKF at abrupt changes in vehicle motion state. The performance of the algorithm is verified by simulating road scenes and building a simulation environment. In order to prove the improvement of the tracking accuracy of the algorithm under actual road conditions, the radar road test is also carried out, and the effectiveness of the algorithm is further verified by the vehicle data obtained by the radar on the road. The results show that the algorithm improves both distance tracking accuracy and speed tracking accuracy when the motion states of the target vehicle are changing.

TN9571.52