Population based particle filtering

This paper proposes a novel particle filtering strategy by combining population Monte Carlo Markov chain methods with sequential Monte Carlo chain particle which we call evolving population Monte Carlo Markov Chain (EP MCMC) filtering. Iterative convergence on groups of particles (populations) is obtained using a specified kernel moving particles toward more likely regions. The proposed technique introduces variety in the particles both in the sampling procedure and in the resampling step. The proposed EP MCMC filter is compared with the generic particle filter [1], with a population MCMC [2] and a sequential Monte Carlo sampler [2]. Its effectiveness is illustrated over an example for object tracking in video sequences and over the bearing only tracking problem.