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概率假设密度高斯混合实现的分量删减

闫小喜 韩崇昭

闫小喜, 韩崇昭. 概率假设密度高斯混合实现的分量删减. 自动化学报, 2011, 37(11): 1313-1321. doi: 10.3724/SP.J.1004.2011.01313
引用本文: 闫小喜, 韩崇昭. 概率假设密度高斯混合实现的分量删减. 自动化学报, 2011, 37(11): 1313-1321. doi: 10.3724/SP.J.1004.2011.01313
YAN Xiao-Xi, HAN Chong-Zhao. Component Pruning in Gaussian Mixture Implementation of Probability Hypothesis Density. ACTA AUTOMATICA SINICA, 2011, 37(11): 1313-1321. doi: 10.3724/SP.J.1004.2011.01313
Citation: YAN Xiao-Xi, HAN Chong-Zhao. Component Pruning in Gaussian Mixture Implementation of Probability Hypothesis Density. ACTA AUTOMATICA SINICA, 2011, 37(11): 1313-1321. doi: 10.3724/SP.J.1004.2011.01313

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
详细信息
    通讯作者:

    闫小喜 西安交通大学电子与信息工程学院综合自动化研究所博士研究生. 主要研究方向为多源信息融合, 多目标跟踪和随机有限集.E-mail: yanxiaoxi1981@gmail.com

Component Pruning in Gaussian Mixture Implementation of Probability Hypothesis Density

  • 摘要: 针对概率假设密度(Probability hypothesis density, 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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  • 收稿日期:  2010-12-01
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