An improved PHD filter based on variational Bayesian method for multi-target tracking

Author

Guanghua Zhang ; Feng Lian ; Chongzhao Han ; Suying Han

Author_Institution

Sch. of Electron. & Inf. Eng., Xi´an Jiaotong Univ., Xi´an, China

fYear

2014

fDate

7-10 July 2014

Firstpage

1

Lastpage

6

Abstract

This paper presents an improved probability hypothesis density (PHD) filter for the multi-target tracking scenarios with unknown measurement noise variances. By introducing the variational Bayesian (VB) method into the PHD recursion, not only the states and number of targets, but also the measurement noise variances can be jointly estimated. Moreover, a closed-form solution to the improved PHD filter for linear Gaussian multi-target model is derived using inverse Gamma and Gaussian mixtures. Simulation results demonstrate the effectiveness of the proposed algorithm for the multi-target tracking scenarios with unknown measurement noise variances.

Keywords

Bayes methods; Gaussian processes; gamma distribution; target tracking; Gaussian mixture; PHD recursion; closed-form solution; improved PHD filter; inverse Gamma mixture; linear Gaussian multitarget model; measurement noise variances; multitarget tracking scenarios; probability hypothesis density filter; variational Bayesian method; Approximation methods; Atmospheric measurements; Bayes methods; Joints; Noise; Noise measurement; Target tracking; Gaussian mixture (GM); Inverse Gamma distribution; Multi-target tracking; Probability hypothesis density (PHD) filter; Variational Bayesian (VB);

fLanguage

English

Publisher

ieee

Conference_Titel

Information Fusion (FUSION), 2014 17th International Conference on

Conference_Location

Salamanca

Type

conf

Filename

6915986