基于多核相关滤波X波段雷达多目标跟踪算法

An Multi-target Tracking Algorithm for X-band Radar Based on Multi-kernel Correlation Filtering

针对雷达X波段数据存在较大背景及杂波干扰、目标形变以及目标被遮挡和相互干扰,导致目标被漏检错检,从而出现漏跟和错跟问题。提出一种基于多核相关滤波的多扩展目标优化跟踪算法,引入多帧速度信息卡尔曼滤波和融合交并比的距离测度,对存活目标、新生目标和消失目标进行自适应识别与更新,利用全局和局部检测的三步法进行航迹估计,此外,提出了多核相关滤波模板优化方法,将模板创建时间转化为自适应权重,从极大似然的角度优化模板融合,能够有效适应对复杂形变多扩展目标跟踪和对漏跟目标的重识别。实验结果表明,提出算法对X波段雷达多目标具有较高的跟踪鲁棒性较好。

X-band radar data are associated with severe background and clutter interference,target deformation,as well as obscure target and mutual interference.These issues often result in the target being missed or wrongly detected.In this study,we propose a multiple extended target optimization tracking algorithm based on a multi-kernel correlation filtering(MKCF).The proposed algorithm comprises both threshold and watershed methods for global and local detection of X-band radar targets,respectively.Then,Kalman filtering(KF)is introduced for adaptive identification and tracking of survival targets,newborn targets and disappearing targets.The KF includes multi-frame velocity information and the distance measure fused cross-parallel ratio.In addition,we propose a template optimization method for MKCF that can convert the template creation time into an adaptive weight and optimize the template fusion with respect to great likelihood.This template optimization method can effectively adapt to multiple extended target tracking with complex deformation;it can also adapt to the re-identification of missing targets.Our experimental results show that the proposed algorithm has high tracking robustness for X-band radar multi-target tracking.