中文摘要：

传统跟踪方法仅利用目标的位置信息进行数据关联,在处理多个目标航迹接近或交叉的情况时容易产生航迹合并甚至误跟的现象.笔者针对此问题提出一种高分辨距离像辅助的多目标跟踪算法.首先利用高分辨距离像的姿态敏感性对目标姿态角进行实时估计,然后将姿态角信息融合到目标的观测状态中,构建多维关联波门,利用多维信息进行数据关联,从而将一个多目标数据关联的问题简化为多个单目标数据关联的问题,最后采用概率数据关联——不敏卡尔曼滤波器分别估计各个目标的运动状态.仿真结果表明,通过对目标高分辨距离像信息的充分利用,不仅可以降低多目标数据关联的复杂度,提高数据关联的正确率,而且在姿态角信息的辅助下可以明显提高目标的跟踪精度.

英文摘要：

When the tracks of the multi-target get approached or crossed,it is easy to lead to combining or even to get wrong tracks for the traditional tracking methods,since the traditional methods only utilize the information on the target position to finish the data association.Aiming at this problem,a multi-target tracking algorithm aided by the high resolution range profile（HRRP）is proposed in this paper.Firstly,the target attitude angle is estimated in real time on the principle that the HRRP is sensitive to the attitude angle.And then the attitude angle is added to the target measurement state to construct a multi-dimension correlating gate.The data association is accomplished with the multi-dimension information.So the problem of multi-target data association is simplified to multiple sub-problems of data association for a single target.Finally,each target motion state is estimated by the probabilistic data association-unscented Kalman filter（PDA-UKF）.Simulation results reveal that the computing complexity is reduced,and that the correct probability of data association is improved by using the target HRRP on the one hand.On the other hand,the tracking accuracy is improved with the aid of the target attitude angle.