均方根嵌入式容积粒子PHD多目标跟踪方法  

Square-root Imbedded Cubature Particle PHD Multi-target Tracking Algorithm

作　　者：熊志刚[1] 黄树彩[1] 赵炜[1] 苑智玮 徐晨洋 

机构地区：[1]空军工程大学防空反导学院,西安710051

出　　处：《自动化学报》2017年第2期238-247,共10页Acta Automatica Sinica

基　　金：国家自然科学基金(61503408;61573374);陕西省自然科学基础研究计划(2012JM8020);航空科学基金(20130196004)资助~~

摘　　要：针对基于概率假设密度算法(Probability hypothesis density,PHD)的非线性多目标跟踪精度低、滤波发散等问题,提出了一种新的PHD算法—改进的均方根嵌入式容积粒子PHD算法(Advanced square-root imbedded cubature particle PHD,ASRICP-PHD).新的算法在初始化采样时将整个采样区域等概率划分为若干个区域,然后利用既定的准则从每个区域抽取粒子,并利用均方根嵌入式容积滤波方法对每个粒子进行滤波,来拟合重要密度函数,预测和更新多目标状态的PHD.仿真结果表明该算法能对多目标进行有效跟踪,相比拟随机采样法和伪随机采样,等概率采样的方法在多目标位置估计和数目估计上有更高的精度.Considering the low accuracy, filter divergence and other problems of nonlinear multi-target tracking based on probability hypothesis density （PHD）, a new filter named advanced square-root imbedded cubature particle PHD （ASRICP-PHD） is proposed. ASRICP-PHD divides the whole particle sampling area into several parts of equal probability, then uses a special rule to obtain particles from each part, and matches the important density function with square-root imbedded cubature particle filter, and therefore predicts and updates PHD. Simulation shows that ASRICP-PHD is able to track multiple targets effectively. Moreover, compared with quasi random sampling, the method of particle sampling based on probability has higher accuracy in terms of multi-target positions and number＇s estimations.

关 键 词：多目标跟踪 概率假设密度 均方根嵌入式容积滤波 等概率采样 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]