Weight over-estimation problem in GMP-PHD filter

Author

Ouyang, Chunmei ; Ji, H.B.

Author_Institution

Sch. of Electron. Eng., Xidian Univ., Xi´an, China

Volume

47

Issue

2

fYear

2011

fDate

1/1/2011 12:00:00 AM

Firstpage

139

Lastpage

141

Abstract

The Gaussian mixture particle probability hypothesis density (GMP-PHD) filter is a promising nonlinear multi-target tracking algorithm. However, when the variance of measurement noise is small, and if there are some particles nearby clutters, the average weight of the particles will be much greater than the clutter density, because the peak value of the likelihood function is much greater than the number of particles. Therefore, the weights of Gaussian components updated by the clutter will be greater than the actual values. The present authors call this phenomenon the weight over-estimation problem, which can be solved by some modifications of the weight updating formula. Simulation results show that the proposed algorithm has better performance than the GMP-PHD filter, implying good application prospects.

Keywords

Gaussian distribution; clutter; filtering theory; maximum likelihood estimation; noise measurement; probability; target tracking; Gaussian mixture particle probability hypothesis density filter; clutter density; likelihood function; measurement noise; nonlinear multi-target tracking algorithm; weight over-estimation problem; weight updating formula;

fLanguage

English

Journal_Title

Electronics Letters

Publisher

iet

ISSN

0013-5194

Type

jour

DOI

10.1049/el.2010.7410

Filename

5700022