On MCMC-Based Particle Methods for Bayesian Filtering : Application to Multitarget Tracking

François Septier (1) , Sze Kim Pang (1) , Avishy Carmi (1) , Simon Godsill (1)

1 Signal Processing Laboratory

Nonlinear non-Gaussian state-space models arise in numerous applications in control and signal processing. In this context, one of the most successful and popular approximation techniques is sequential Monte Carlo (SMC) methods, also known as particle filters. Nevertheless, these methods tend to be inefficient when applied to high dimensional problems. In this paper, we present an overview of Markov chain Monte Carlo (MCMC) methods for sequential simulation from posterior distributions, which represent efficient alternatives to SMC methods. Then, we describe an implementation of this MCMC-Based particle algorithm to perform the sequential inference for multitarget tracking. Numerical simulations illustrate the ability of this algorithm to detect and track multiple targets in a highly cluttered environment.

Mots clés: Bayesian filtering; Markov chain Monte Carlo methods; multitarget tracking; nonlinear nonGaussian state-space model; particle methods; sequential Monte Carlo methods; signal processing

Domaines: Traitement du signal et de l'image [eess.SP]; Traitement du signal et de l'image [eess.SP]

François Septier, Sze Kim Pang, Avishy Carmi, Simon Godsill. On MCMC-Based Particle Methods for Bayesian Filtering : Application to Multitarget Tracking. IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), Dec 2009, Aruba, Netherlands Antilles. pp.1280-1287, ⟨10.1109/CAMSAP.2009.5413256⟩. ⟨hal-00566647⟩