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Introduction to the Box Particle Filtering

Gning, Amadou and Ristic, B and Mihaylova, Lyudmila and Abdallah, F. (2013) Introduction to the Box Particle Filtering. IEEE Signal Processing Magazine, 30 (4). pp. 166-171.

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    This paper presents a novel method for solving nonlinear filtering problems. This approach is particularly appealing in practical situations involving imprecise stochastic measurements, thus resulting in very broad posterior densities. It relies on the concept of a box particle, which occupies a small and controllable rectangular region having a non-zero volume in the state space. Key advantages of the box particle filter (Box-PF) against the standard particle filter (PF) are in its reduced computational complexity and its suitability for distributed filtering. Indeed, in some applications where the sequential importance resampling (SIR) PF may require thousands of particles to achieve an accurate and reliable performance, the Box-PF can reach the same level of accuracy with just a few dozens of box particles.

    Item Type: Journal Article
    Journal or Publication Title: IEEE Signal Processing Magazine
    Uncontrolled Keywords: sequential Monte Carlo methods ; Particle methods ; uncertainty ; Uniform distribution ; Box particle filter ; tracking
    Subjects: ?? qa75 ??
    Departments: Faculty of Science and Technology > School of Computing & Communications
    ID Code: 53537
    Deposited By: ep_importer_pure
    Deposited On: 18 Apr 2012 11:45
    Refereed?: Yes
    Published?: Published
    Last Modified: 19 Sep 2018 00:17
    Identification Number:

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