Particle flters for probability hypothesis density flter with the presence of unknown measurement noise covariance  

作　　者：Wu Xinhui Huang Gaoming Gao Jun 

机构地区：[1]College of Electronic Engineering, Naval University of Engineering [2]Automatization of Command Institute, Academy of Navy Equipment

出　　处：《Chinese Journal of Aeronautics》2013年第6期1517-1523,共7页中国航空学报（英文版）

基　　金：supported by National High-tech Research and Development Program of China (No.2011AA7014061)

摘　　要：In Bayesian multi-target fltering,knowledge of measurement noise variance is very important.Signifcant mismatches in noise parameters will result in biased estimates.In this paper,a new particle flter for a probability hypothesis density（PHD）flter handling unknown measurement noise variances is proposed.The approach is based on marginalizing the unknown parameters out of the posterior distribution by using variational Bayesian（VB）methods.Moreover,the sequential Monte Carlo method is used to approximate the posterior intensity considering non-linear and non-Gaussian conditions.Unlike other particle flters for this challenging class of PHD flters,the proposed method can adaptively learn the unknown and time-varying noise variances while fltering.Simulation results show that the proposed method improves estimation accuracy in terms of both the number of targets and their states.In Bayesian multi-target fltering,knowledge of measurement noise variance is very important.Signifcant mismatches in noise parameters will result in biased estimates.In this paper,a new particle flter for a probability hypothesis density（PHD）flter handling unknown measurement noise variances is proposed.The approach is based on marginalizing the unknown parameters out of the posterior distribution by using variational Bayesian（VB）methods.Moreover,the sequential Monte Carlo method is used to approximate the posterior intensity considering non-linear and non-Gaussian conditions.Unlike other particle flters for this challenging class of PHD flters,the proposed method can adaptively learn the unknown and time-varying noise variances while fltering.Simulation results show that the proposed method improves estimation accuracy in terms of both the number of targets and their states.

关 键 词：Multi-target tracking（MTT） Parameter estimation Probability hypothesis density Sequential Monte Carlo Variational Bayesian method 

分 类 号：TN911.4[电子电信—通信与信息系统] O212.1[电子电信—信息与通信工程]