均方根嵌入式容积粒子PHD多目标跟踪方法
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,...
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| Published in | 自动化学报 Vol. 43; no. 2; pp. 238 - 247 |
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| Main Author | |
| Format | Journal Article |
| Language | Chinese |
| Published |
空军工程大学防空反导学院 西安710051
2017
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| Subjects | |
| Online Access | Get full text |
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| Summary: | 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. |
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| Bibliography: | 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. Multi-target tracking, probability hypothesis density （PHD）, square-root imbedded cubature filter, sampling with equal probability XIONG Zhi-Gang1, HUANG Shu-Cai1, ZHAO Wei1, YUAN Zhi-Wei1 ,XU Chen-Yang1 11-2109/TP |
| ISSN: | 0254-4156 1874-1029 |
| DOI: | 10.16383/j.aas.2017.c150881 |