当采用概率母函数将单传感器PHD 滤波推广到多传感器情形时，针对计算繁琐，难于实现的问题，本文基于集中式融合系统的有序滤波思想，提出多传感器、多目标有序粒子PHD 跟踪算法，该算法通过选取与各传感器相关的重要性密度函数，层层更新各传感器的采样粒子，达到多传感器多目标有序PHD 跟踪。实验结果表明，当仅仅使用单传感器对多目标进行跟踪时，虚警概率较高时一些粒子会严重偏离原始目标轨迹，导致目标数目估计出现偏差，而采用多传感器多目标有序PHD 跟踪可以有效减小多目标距离跟踪误差，提高跟踪精度。

Aiming at complicated computing and difficulty of being realized when extending single sensor PHD filtering to the multi-sensor case by means of probability generating function, a multi-sensor multi-target sequential particle-PHD tracking algorithm is proposed based on the thought of sequential filtering for a centralized fusion system. The algorithm chooses the importance density function with regard to every sensor, and updates sample particle of every sensor layer by layer. Finally, the multi-sensor multi-target sequential PHD tracking is realized. Experimental results show, when multi-target is tracked only using single sensor, some particles can deviate true trajectories of target, which causes the error of estimated numbers of targets. However, the multi-sensor multi-target sequential particle-PHD tracking algorithm can reduce distance error and improve tracking accuracy effectively.