摘要
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.
出处
《光电工程》
CAS
CSCD
北大核心
2009年第3期22-27,共6页
Opto-Electronic Engineering
基金
国家自然科学基金资助项目(60678018)
陕西省自然科学基金资助项目(SJ08F10)
关键词
多目标跟踪
集中式融合系统
多传感器
概率假设密度
粒子滤波
multi-target tracking
centralized fusion system
multi-sensor
probability hypothesis density
particle filtering
