Marginalized particle PHD filters for multiple object bayesian filtering

Yohan Petetin; Mark Morelande; Francois Desbouvries

doi:10.1109/TAES.2014.120805

Marginalized particle PHD filters for multiple object bayesian filtering

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Article number	6850148
Pages (from-to)	1182-1196
Number of pages	15
Journal	IEEE Transactions on Aerospace and Electronic Systems
Volume	50
Issue number	2
DOIs	https://doi.org/10.1109/TAES.2014.120805
Publication status	Published - 1 Jan 2014

Access to Document

10.1109/TAES.2014.120805

Cite this

@article{d297fe4037ce45339f19c973d47afdce,

title = "Marginalized particle PHD filters for multiple object bayesian filtering",

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.",

author = "Yohan Petetin and Mark Morelande and Francois Desbouvries",

year = "2014",

month = jan,

day = "1",

doi = "10.1109/TAES.2014.120805",

language = "English",

volume = "50",

pages = "1182--1196",

journal = "IEEE Transactions on Aerospace and Electronic Systems",

issn = "0018-9251",

number = "2",

}

TY - JOUR

T1 - Marginalized particle PHD filters for multiple object bayesian filtering

AU - Petetin, Yohan

AU - Morelande, Mark

AU - Desbouvries, Francois

PY - 2014/1/1

Y1 - 2014/1/1

N2 - 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.

AB - 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.

U2 - 10.1109/TAES.2014.120805

DO - 10.1109/TAES.2014.120805

M3 - Article

AN - SCOPUS:84904725084

SN - 0018-9251

VL - 50

SP - 1182

EP - 1196

JO - IEEE Transactions on Aerospace and Electronic Systems

JF - IEEE Transactions on Aerospace and Electronic Systems

IS - 2

M1 - 6850148

ER -

Marginalized particle PHD filters for multiple object bayesian filtering

Abstract

Access to Document

Fingerprint

Cite this