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Particle algorithms for filtering in high dimensional state space : a case study in group object tracking.

Mihaylova, Lyudmila and Carmi, Avishy (2011) Particle algorithms for filtering in high dimensional state space : a case study in group object tracking. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, Prague, pp. 5932-5935. ISBN 978-1-4577-0537-3

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Abstract

We briefly present the current state-of-the-art approaches for group and extended object tracking with an emphasis on particle methods which have high potential to handle complex structured scenarios. The big dimensionality attributed to the group tracking problem poses a major difficulty to particle filters (PFs). This in turn has motivated researchers to devise many alternatives and variants over the past decade. In this work, we corroborate and extend a single promising direction for alleviating the dimensionality problem. Our derived scheme endows a recently introduced Markov chain Monte Carlo (MCMC) PF algorithm with an improved proposal distribution. We demonstrate the performance of our approach using a nonlinear system with up to 40 states.

Item Type: Contribution in Book/Report/Proceedings
Uncontrolled Keywords: nonlinear estimation ; sequential Monte Carlo methods ; Markov chain Monte Carlo methods (MCMC) ; high dimensional systems ; group object tracking
Subjects: Q Science > QA Mathematics
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 40837
Deposited By: Dr L Mihaylov
Deposited On: 31 May 2011 09:04
Refereed?: No
Published?: Published
Last Modified: 23 Sep 2013 15:50
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/40837

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