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概率假设密度高斯混合实现的分量删减

DOI: 10.3724/SP.J.1004.2011.01313, PP. 1313-1321

Keywords: 概率假设密度,高斯混合实现,分量删减,Dirichlet分布,极大后验

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Abstract:

?针对概率假设密度(Probabilityhypothesisdensity,PHD)高斯混合实现算法中的分量删减问题,提出了基于Dirichlet分布的分量删减算法以改进概率假设密度高斯混合实现算法的性能.算法采用极大后验准则估计混合参数,采用仅依赖于混合权重的负指数Dirichlet分布作为混合参数的先验分布,利用拉格朗日乘子推导了混合权重的更新公式.算法利用负指数Dirichlet分布的不稳定性,在极大后验迭代过程中驱使与目标强度不相关的分量消亡.该不稳定性还能够解决多个相近分量共同描述一个强度峰值的问题,有利于后续多目标状态的提取.仿真结果表明,基于Dirichlet分布的分量删减算法优于典型高斯混合实现中的删减算法.

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