Marginalized particle PHD filters for multiple object Bayesian filtering

Author

Petetin, Yohan ; Morelande, Mark ; Desbouvries, Francois

Author_Institution

CITI Dept., Mines Telecom Inst., Evry, France

Volume

50

Issue

2

fYear

2014

fDate

Apr-14

Firstpage

1182

Lastpage

1196

Abstract

The Probability Hypothesis Density (PHD) filter is a recent solution to the multi-target filtering problem. Because the PHD filter is not computable, several implementations have been proposed including the Gaussian Mixture (GM) approximations and Sequential Monte Carlo (SMC) methods. In this paper, we propose a marginalized particle PHD filter which improves the classical solutions when used in stochastic systems with partially linear substructure.

Keywords

Bayes methods; Gaussian processes; Monte Carlo methods; mixture models; particle filtering (numerical methods); GM approximations; Gaussian mixture approximations; SMC methods; marginalized particle PHD filters; multiple object Bayesian filtering; multitarget filtering problem; partially linear substructure; probability hypothesis density filter; sequential Monte Carlo methods; stochastic systems; Adaptation models; Approximation methods; Atmospheric measurements; Bayes methods; Filtering; Particle measurements; Vectors;

fLanguage

English

Journal_Title

Aerospace and Electronic Systems, IEEE Transactions on

Publisher

ieee

ISSN

0018-9251

Type

jour

DOI

10.1109/TAES.2014.120805

Filename

6850148