利用方向角信息辅助雷达目标跟踪

Aspect angle-aided tracking for radar targets

传统雷达仅能提供目标的方位和距离量测,由于可利用的信息相对较少,跟踪精度很难进一步提高。利用现代雷达的高分辨探测能力,提出了一种基于距离像识别信息辅助目标跟踪的模型,并结合求根不敏卡尔曼滤波技术得到了一种高性能跟踪算法。该算法根据距离像识别结果得到目标方向角的测量,进而通过增加观测量的维数来提高目标的跟踪能力。不同条件下的仿真结果表明,利用方向角信息辅助的跟踪算法收敛速度快,跟踪精度高,且复杂度与传统算法相当。

In traditional radar target tracking, only azimuth and range measurement data are used, and the tracking precision can not be further improved because of insufficient target information. For this problem, utili- zing the ability of high resolution sensing of modern radar, a high-performance target tracking model and imple mentation algorithm is proposed by integrating the square-root unscented Kalman filter （SRUKF）. The algo- rithm is based on target＇s aspect angle （referred to as feature information）, which is a derived measurement from a high range resolution profile （HRRP） recognition. The performance improvement of tracking comes from this extra feature information, which is inherently related with target motion state. Simulation results show that the presented method has fast convergence speed, high tracking precision and low computational cost compared with the traditional one.