多普勒信息辅助的杂波环境下多目标跟踪算法

传统雷达目标跟踪方法仅利用目标的位置信息进行数据关联，在处理密集杂波环境下的多目标跟踪问题时，容易产生虚假航迹，甚至出现误跟、失跟的现象。该文针对此问题提出一种多普勒信息辅助的杂波环境下多目标跟踪算法。首先引入多普勒信息带来的观测方程非线性，以及速度观测与距离观测之间的相关性问题，基于IPDA-UKF算法框架，综合利用目标的位置和速度信息构建多维关联波门，利用多维信息进行有效量测的筛选，从而将一个多目标数据关联的问题转化为多个单目标数据关联的问题，然后采用IPDA-UKF分别估计各个目标的存在概率和运动状态。仿真结果表明:相比其他算法，该算法充分利用距离和速度之间的相关性，不仅降低了杂波环境下多目标数据关联的复杂度，提高了数据关联的效率，而且目标跟踪精度也得到了明显提升。

Multi-Target Tracking in Clutter Aided by Doppler Information

The traditional radar target tracking methods only utilize the information of target position to finish data association. When these methods are used to deal with the problem of multi-target tracking in the dense clutter, it is easy to generate the false tracks or even to lose tracks. Aiming at this problem, a multi-target tracking algorithm aided by Doppler information is proposed in this paper. The problems of the nonlinear measurement and the correlation relationship between range and Doppler measurements are considered in the proposed algorithm. Firstly, the multi-dimension correlating gate is constructed with the information of target position and velocity based on the frame of integrated probabilistic data association and unscented Kalman filter (IPDA-UKF). The data association is accomplished with the multi-dimension information. So the problem of multi-target data association is simplified to multiple sub-problems consisting of a single target data association. Secondly, the existing probability and motion state of each target are estimated by the IPDA-UKF algorithm respectively. The simulation results and comparison with the other algorithms reveal that the proposed algorithm has reduced the computing complexity of multi-target data association, and improved the efficiency of data association by using the correlation between range and Doppler measurement completely on the one hand. On the other hand, the tracking accuracy is also improved by the aid of Doppler information.