Millimeter-wave radar has been widely used in intelligent vehicle target detection. However, there are three difficulties in radar-based target tracking in curves. First, there are massive data association calculations with poor accuracy. Second, the lane position relationship of target-vehicle cannot be identified accurately. Third, the target tracking algorithm has poor robustness and accuracy. A target tracking algorithm framework on curved road is proposed herein. The following four algorithms are applied to reduce data association calculations and improve accuracy. (1) The data rationality judgment method is employed to eliminate target measurement data outside the radar detection range. (2) Effective target life cycle rules are used to eliminate false targets and clutter. (3) Manhattan distance clustering algorithm is used to cluster multiple data into one. (4) The correspondence between the measurement data received by the radar and the target source is identified by the nearest neighbor (NN) data association. The following three algorithms aim to derive the position relationship between the ego-vehicle and the target-vehicles. (1) The lateral speed is obtained by estimating the state of motion of the ego-vehicle. (2) An algorithm for state compensation of target motion is presented by considering the yaw motion of the ego-vehicle. (3) A target lane relationship recognition model is built. The improved adaptive extended Kalman filter (IAEKF) is used to improve the target tracking robustness and accuracy. Finally, the vehicle test verifies that the algorithms proposed herein can accurately identify the lane position relationship. Experiments show that the framework has higher target tracking accuracy and lower computational time.

中文翻译：

---

基于毫米波雷达的弯道目标跟踪算法研究

毫米波雷达已广泛用于智能车辆目标检测。但是，在基于雷达的曲线目标跟踪中存在三个困难。首先，存在大量的数据关联计算，但准确性较差。其次，目标车辆的车道位置关系无法准确识别。第三，目标跟踪算法的鲁棒性和准确性较差。本文提出了一种弯道目标跟踪算法框架。应用以下四种算法来减少数据关联计算并提高准确性。（1）采用数据合理性判断方法消除雷达探测范围以外的目标测量数据。（2）有效的目标生命周期规则用于消除虚假目标和混乱情况。（3）采用曼哈顿距离聚类算法将多个数据聚为一个。（4）雷达接收的测量数据与目标源之间的对应关系由最近邻（NN）数据关联来标识。以下三种算法旨在推导自我车辆与目标车辆之间的位置关系。（1）横向速度是通过估计自我车辆的运动状态而获得的。（2）提出了一种考虑自我车辆的偏航运动的目标运动状态补偿算法。（3）建立目标车道关系识别模型。改进的自适应扩展卡尔曼滤波器（IAEKF）用于提高目标跟踪的鲁棒性和准确性。最后，车辆测试证明本文提出的算法可以准确识别车道位置关系。实验表明，该框架具有较高的目标跟踪精度和较低的计算时间。