Stochastic simulation Bayesian approach to multitarget tracking

Abstract

A stochastic simulation Bayesian method for multitarget tracking is developed. This method uses a random sample in state space to represent the posterior state estimate distribution. The method is illustrated by simulations involving one target in dense clutter. Comparison with nearest-neighbours and probabilistic data association shows the superiority of the proposed method.