自动化学报2011，Vol.37Issue(11)：1313-1321,9.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
Component Pruning in Gaussian Mixture Implementation of Probability Hypothesis Density
摘要
Abstract
As far as component pruning in Gaussian mixture (GM) implementation of probability hypothesis density (PHD) is concerned, a component pruning algorithm based on Dirichlet distribution is proposed to improve the performance of Gaussian mixture implementation of probability hypothesis density. The maximum a posterior criterion is adopted for estimation of mixing parameters. Dirichlet distribution with negative exponent parameters, which only depends on mixing weights, is adopted as the prior distribution of mixing parameters. The update formulation of mixing weight is derived by Lagrange multiplier. The instability of Dirichlet distribution with negative exponent parameters is applied to driving the components irrelevant with target intensity to extinction during the maximum a posterior iteration. Besides, the problem that one peak of intensity is presented by several proximate mixing component, can be solved by this instability. It is useful for the following state extraction. Simulation results show that the component pruning algorithm based on Dirichlet distribution is superior to that of typical Gaussian mixture implementation.关键词
概率假设密度/高斯混合实现/分量删减/Dirichlet分布/极大后验Key words
Probability hypothesis density (PHD)/ Gaussian mixture implementation/ component pruning/ Dirichlet distribution/ maximum a posterior (MAP)引用本文复制引用
闫小喜,韩崇昭..概率假设密度高斯混合实现的分量删减[J].自动化学报,2011,37(11):1313-1321,9.基金项目
国家重点基础研究发展计划(973计划)(2007CB311006),国家自然科学基金创新研究群体科学基金(60921003),国家自然科学基金(61074176)资助 （973计划）
