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.
Marginalized particle PHD filters for multiple object Bayesian filtering
IEEE Transactions on Aerospace and Electronic Systems ; 50 , 2 ; 1182-1196
01.04.2014
696626 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Marginalized particle filters for mixed linear/nonlinear state-space models
Tema Archiv | 2005
|Three-Degree-of-Freedom Estimation of Agile Space Objects Using Marginalized Particle Filters
Online Contents | 2017
|The marginalized particle filter in practice
IEEE | 2006
|