Abstract
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.
Original language | Undefined |
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Title of host publication | Proceedings of the 15th International Conference on Information Fusion (FUSION 2012) |
Place of Publication | Singapore |
Publisher | IEEE |
Pages | 74-81 |
Number of pages | 8 |
ISBN (Print) | 978-1-4673-0417-7 |
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 |
Publication series
Name | |
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Publisher | IEEE |
Conference
Conference | 15th International Conference on Information Fusion, FUSION 2012 |
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Abbreviated title | FUSION 2012 |
Country/Territory | Singapore |
City | Singapore |
Period | 9/07/12 → 12/07/12 |
Other | 9-12 July 2012 |
Keywords
- IR-83551
- EWI-22762
- METIS-296185