Population based particle filtering

This paper proposes a novel particle filtering strategy by combining population Monte Carlo Markov chain methods with sequential Monte Carlo chain particle which we call evolving population Monte Carlo Markov Cham (EP MCMC) filtering. Iterative convergence on groups of particles (populations) is obt...

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Bibliographic Details
Published in:	IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications pp. 29 - 38
Main Authors:	Bhaskar, H, Mihaylova, L, Maskell, S
Format:	Conference Proceeding
Language:	English
Published:	Stevenage IET 2008
Subjects:	Filtering methods in signal processing Markov processes Monte Carlo methods Signal processing theory Monte Carlo methods iterative convergence population based particle filtering sequential Monte Carlo chain particle Markov processes particle filtering (numerical methods) Monte Carlo Markov Chain filtering
ISBN:	0863419100, 9780863419102
Online Access:	Get full text
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Summary:	This paper proposes a novel particle filtering strategy by combining population Monte Carlo Markov chain methods with sequential Monte Carlo chain particle which we call evolving population Monte Carlo Markov Cham (EP MCMC) filtering. Iterative convergence on groups of particles (populations) is obtained using a specified kernel moving particles toward more likely regions. The proposed technique introduces variety in the particles both in the sampling procedure and in the resampling step. The proposed EP MCMC filter is compared with the generic particle filter, with a population MCMC by A. Jastra et al (2007) and a sequential Monte Carlo sampler. Its effectiveness is illustrated over an example for object tracking in video sequences and over the bearing only tracking problem.
ISBN:	0863419100 9780863419102
DOI:	10.1049/ic:20080054