Title :
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
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;
Journal_Title :
Aerospace and Electronic Systems, IEEE Transactions on
DOI :
10.1109/TAES.2014.120805
