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    A smarter particle filter

    Zhang, X. and Hu, W. and Maybank, Stephen J. (2010) A smarter particle filter. In: Zha, H and Taniguchi, R.-i. and Maybank, Stephen J. (eds.) Computer Vision. Berlin, Germany: Springer Verlag, pp. 236-246. ISBN 9783642123047.

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    Particle filtering is an effective sequential Monte Carlo approach to solve the recursive Bayesian filtering problem in non-linear and non-Gaussian systems. The algorithm is based on importance sampling. However, in the literature, the proper choice of the proposal distribution for importance sampling remains a tough task and has not been resolved yet. Inspired by the animal swarm intelligence in the evolutionary computing, we propose a swarm intelligence based particle filter algorithm. Unlike the independent particles in the conventional particle filter, the particles in our algorithm cooperate with each other and evolve according to the cognitive effect and social effect in analogy with the cooperative and social aspects of animal populations. Furthermore, the theoretical analysis shows that our algorithm is essentially a conventional particle filter with a hierarchial importance sampling process which is guided by the swarm intelligence extracted from the particle configuration, and thus greatly overcome the sample impoverishment problem suffered by particle filters. We compare the proposed approach with several nonlinear filters in the following tasks: state estimation, and visual tracking. The experiments demonstrate the effectiveness and promise of our approach.


    Item Type: Book Section
    School: School of Business, Economics & Informatics > Computer Science and Information Systems
    Depositing User: Sarah Hall
    Date Deposited: 25 Jul 2013 12:37
    Last Modified: 11 Sep 2013 12:39


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