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Box-particle intensity filter

Schikora, Marek and Gning, Amadou and Mihaylova, Lyudmila and Cremers, Daniel and Koch, Wofgang and Streit, Roy (2012) Box-particle intensity filter. In: Data Fusion & Target Tracking Conference (DF&TT 2012): Algorithms & Applications, 9th IET :. .

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    This paper develops a novel approach for multi-target tracking, called box-particle intensity filter (box-iFilter). The approach is able to cope with unknown clutter, false alarms and estimates the unknown number of targets. Furthermore, it is capable of dealing with three sources of uncertainty: stochastic, set-theoretic and data association uncertainty. The box-iFilter reduces the number of particles significantly, which improves the runtime considerably. The low particle number enables this approach to be used for distributed computing. A box-particle is a random sample that occupies a small and controllable rectangular region of non-zero volume. Manipulation of boxes utilizes the methods from the field of interval analysis. Our studies suggest that the box-iFilter reaches an accuracy similar to a sequential Monte Carlo (SMC) iFilter but with much less computational costs.

    Item Type: Contribution in Book/Report/Proceedings
    Uncontrolled Keywords: Multi-Target Tracking ; Box Particle Filters ; Poisson Point Processes ; Intensity Filter ; interval measurements
    Subjects: ?? qa75 ??
    Departments: Faculty of Science and Technology > School of Computing & Communications
    ID Code: 54409
    Deposited By: ep_importer_pure
    Deposited On: 22 May 2012 10:08
    Refereed?: No
    Published?: Published
    Last Modified: 13 Aug 2018 01:12
    Identification Number:

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