Tracking individuals within a group is one of the major tasks of group target observation. Tracking radar must feature high frame rate and high range-angular resolution to achieve the stable multitarget tracking performance. However, two major problems arise from this scenario. First, the narrow beam of the tracking radar does not allow the complete observation of group target, causing the fluctuation of target number as the radar-target geometry changes; second, false alarms may be target-dependent and distributed around the targets, which is contrary to the traditional spatially uniform clutter model. This article proposes a Gaussian mixture probability hypothesis density (PHD) filter for multitarget tracking using a collaborative radar system. The system consists of one scanning radar and one tracking radar. The former outputs the group's collective states (centroid, extension, etc.), which are used as the priors for the tracking radar. The tracking radar is responsible for the multitarget tracking. The density of target birth and death are set according to the priors. The update equation of the PHD in target-dependent false alarms is derived and simplified to meet the practical application requirements. Finally, the effectiveness of the proposed filter is verified by the simulation and experimental results.

中文翻译：

---

用于目标相关误报中多目标跟踪的高斯混合 PHD 滤波器


跟踪群体内的个体是群体目标观察的主要任务之一。跟踪雷达必须具有高帧率和高距离角分辨率才能实现稳定的多目标跟踪性能。然而，这种情况会产生两个主要问题。首先，跟踪雷达的窄波束不允许对群目标进行完整观测，导致目标数量随着雷达-目标几何形状的变化而波动；其次，误报可能与目标相关并且分布在目标周围，这与传统的空间均匀杂波模型相反。本文提出了一种使用协作雷达系统进行多目标跟踪的高斯混合概率假设密度 (PHD) 滤波器。该系统由一台扫描雷达和一台跟踪雷达组成。前者输出组的集体状态（质心、延伸等），用作跟踪雷达的先验。跟踪雷达负责多目标跟踪。目标出生和死亡的密度是根据先验设定的。推导并简化了目标相关虚警中的PHD更新方程以满足实际应用需求。最后，通过仿真和实验结果验证了所提出滤波器的有效性。