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嵌入式容积粒子PHD多目标跟踪算法
引用本文:熊志刚,黄树彩,赵炜,苑智玮. 嵌入式容积粒子PHD多目标跟踪算法[J]. 信号处理, 2016, 32(6): 676-683. DOI: 10.16798/j.issn.1003-0530.2016.06.006
作者姓名:熊志刚  黄树彩  赵炜  苑智玮
作者单位:空军工程大学防空反导学院,陕西西安 710051
基金项目:陕西省自然科学基础研究计划资助项目(2012JM8020);航空科学基金(20130196004)
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估计精度不高、滤波发散、实时性差等问题,提出一种嵌入式容积粒子PHD算法(Imbedded Cubature Particle PHD,ICP PHD)。新的算法在采样阶段引入Halton点集,并基于三阶嵌入式容积准则产生有限的积分点,对每个采样粒子进行滤波,来拟合重要密度函数。由于Halton点集得到的粒子分布更加均匀,故而ICP PHD算法能够避免 “粒子聚集”的现象。另外,由于三阶嵌入式容积准则的积分点少、精度高,因此ICP PHD算法能更好的协调时间与精度之间的矛盾。仿真结果表明ICP PHD能对多目标进行有效跟踪,相比高斯厄米特粒子PHD算法(Gauss Hermite Particle PHD,GHP PHD)具有实时性强的优势,在目标数目和状态估计上比容积粒子PHD算法(Cubature Particle PHD,CP PHD)精度更高。 

关 键 词:多目标跟踪   概率假设密度   嵌入式求容积准则   Halton点集
收稿时间:2015-11-09

Imbedded Cubature Particle PHD Filter Multi target Tracking Algorithm
Abstract:Considering the low accuracy, filter divergence and poor timeliness of nonlinear multi target tracking based on probability hypothesis density (PHD), a new filter named imbedded cubature particle PHD(ICP PHD) is proposed. ICP PHD implements particle sampling with Halton points sets, and generates infinite integral points based on the third degree imbedded cubature rule to perform particle filtering for the purpose of matching the important density function. As a result of the well distributed particles obtained with Halton sets, ICP PHD can avoid the phenomenon of particle aggregation. Besides, ICP PHD can deal with the contradictions between time and accuracy well because of the few integral points and high accuracy. Simulation was made and it showed that ICP PHD could be able to track multiple targets effectively. Moreover, ICP PHD spent less time compared with Gauss Hermite, and performed better in targets number estimation and state estimation comparing with cubature particle PHD(CP PHD). 
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