Multitarget tracking with IP reversible jump MCMC-PF

Abstract

In this paper we address the problem of tracking multiple targets based on raw measurements by means of Particle filtering. Bayesian multitarget tracking, in the Random Finite Set framework, propagates the multitarget posterior density recursively in time. Sequential Monte Carlo (SMC) approximations of the optimal filter are computationally expensive and lead to high-variance estimates as the number of targets increases. We present an extension of the Interacting Population-based MCMC-PF (IP-MCMC-PF) [1]. This extension exploits reversible jumps. Incorporation of Reversible Jump MCMC (RJMCMC) [2] methods into a tracking framework gives the possibility to deal with multiple appearing and disappearing targets, and makes the statistical inference more tractable. In our case, the technique is adopted to efficiently solve the high-dimensional state estimation problem, where the estimation of the existence and positions of many targets from a sequence of noisy measurements is required. Simulation analyses demonstrate that the proposed IP-RJMCMC-PF yields higher consistency, accuracy and reliability in multitarget tracking