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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Vydáno v:IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications s. 29 - 38
Hlavní autoři: Bhaskar, H, Mihaylova, L, Maskell, S
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: Stevenage IET 2008
Témata:
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
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Shrnutí: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