自动化学报2011，Vol.37Issue(5)：577-584,8.DOI:10.3724/SP.J.1004.2011.00577
基于增量式有限混合模型的多目标状态极大似然估计
Maximum Likelihood Estimation of Multiple Target States Based on Incremental Finite Mixture Model
摘要
Abstract
The incremental finite mixture model (IFMM) is proposed to extract target states in the sequential Monte Carlo implementation of probability hypothesis density (PHD) filter.The proposed model is constructed in an incremental way.The mixture components are inserted into mixture model one after another.Maximum likelihood (ML) criterion is adopted in the model for multiple target state estimation.For the mixture model with given component number,expectation maximum (EM) algorithm is applied in obtaining the maximum likelihood solution of model parameters.When the new component is inserted into the mixture model, maximum likelihood criterion is yet adopted for the selection of new component from the candidate set of new components, while the parameters of existing components in mixture model remain invariable.The step of inserting new component into mixture model and the step of maximum likelihood parameter fitting of mixture model by expectation maximum algorithm are alternately applied until the number of mixture components is equal to the estimate of target number produced by the probability hypothesis density filter.The candidate set of new components for inserting into mixture model is generated by k-dimensional tree.The incremental finite mixture model unifies the tendency of component number and that of likelihood of particle set so that it contributes to searching maximum likelihood solution of mixture model step by step.Simulation results show that the state extraction algorithm based on incremental finite mixture model is superior to the existing algorithms for the probability hypothesis density filter in multiple target tracking.关键词
多目标状态估计/增量式有限混合模型/概率假设密度滤波器/极大似然/期望极大化Key words
Multiple target state estimation/ incremental finite mixture model (IFMM)/ probability hypothesis density (PHD) filter/ maximum likelihood (ML)/ expectation maximum (EM)引用本文复制引用
闫小喜,韩崇昭..基于增量式有限混合模型的多目标状态极大似然估计[J].自动化学报,2011,37(5):577-584,8.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)资助 （973计划）
