Added Markov Chain Monte Carlo particle filtering optimized by Mean Shift and application in target tracking

Bayesian statistics has attracted people´s interest again recently because of the application of Markov Chain Monte Carlo (MCMC) theory. In particle filtering, the diversification of particles disappears in the process of importance sampling. However, this problem can be solved using Metropolis-Hastings (MH) sampling usually used in MCMC theory. As a modification to added MCMC (AMCMC) - an improved MCMC particle filter that can track variable number of targets at the same time, a new approach to optimize those rejected samples in MH sampling process by Mean Shift algorithm is proposed in this paper. Because the operation rate of particles in AMCMC is increased, the circles of sampling needed for the convergence of Markov chain is reduced. It is shown by experiment that, the optimized algorithm has better tracking performance under the condition of fewer particles.