Marginalized particle PHD filters for multiple object Bayesian filtering
Yohan Petetin, Mark R. Morelande, François Desbouvries · IEEE Transactions on Aerospace and Electronic Systems · 2014
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.