In this paper we address the problem of tracking multiple targets based on raw measurements by means of Particle filtering. This strategy leads to a high computational complexity as the number of targets increases, so that an efficient implementation of the tracker is necessary. We propose a new multitarget Particle Filter (PF) that solves such challenging problem. We call our filter Interacting Population-based MCMC-PF (IP-MCMC-PF) since our approach is based on parallel usage of multiple population-based Metropolis-Hastings (M-H) samplers. Furthermore, to improve the chains mixing properties, we exploit genetic alike moves performing interaction between the Markov Chain Monte Carlo (MCMC) chains. Simulation analyses verify a dramatic reduction in terms of computational time for a given track accuracy, and an increased robustness w.r.t. conventional MCMC based PF.
|Title of host publication||Proceedings of the 15th International Conference on Information Fusion (FUSION 2012)|
|Place of Publication||Singapore|
|Number of pages||8|
|Publication status||Published - Jul 2012|
|Event||15th International Conference on Information Fusion, FUSION 2012 - Singapore, Singapore, Singapore|
Duration: 9 Jul 2012 → 12 Jul 2012
Conference number: 15
|Conference||15th International Conference on Information Fusion, FUSION 2012|
|Abbreviated title||FUSION 2012|
|Period||9/07/12 → 12/07/12|
|Other||9-12 July 2012|
Bocquel, M., Driessen, H., & Bagchi, A. (2012). Multitarget tracking with interacting population-based MCMC-PF. In Proceedings of the 15th International Conference on Information Fusion (FUSION 2012) (pp. 74-81). Singapore: IEEE.