Population based particle filtering
Population based particle filtering
- Author(s): H. Bhaskar ; L. Mihaylova ; S. Maskell
- DOI: 10.1049/ic:20080054
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- Author(s): H. Bhaskar ; L. Mihaylova ; S. Maskell Source: IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications, 2008 p. 29 – 38
- Conference: IET Seminar on Target Tracking and Data Fusion: Algorithms and Applications
- DOI: 10.1049/ic:20080054
- ISBN: 978 0 86341 910 2
- Location: Birmingham, UK
- Conference date: 15-16 April 2008
- Format: PDF
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.
Inspec keywords: Monte Carlo methods; Markov processes; particle filtering (numerical methods)
Subjects: Signal processing theory; Markov processes; Markov processes; Monte Carlo methods; Filtering methods in signal processing; Monte Carlo methods
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