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Extended object tracking with convolution particle filtering

Angelova, D. and Mihaylova, L. and Petrov, N. and Gning, A. (2012) Extended object tracking with convolution particle filtering. In: Intelligent Systems (IS), 2012 6th IEEE International Conference :. IEEE, pp. 96-101. ISBN 978-1-4673-2276-8

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    This paper proposes a sequential Monte Carlo filter (particle filter) for state and parameter estimation of dynamic systems. It is applied to the problem of extended object tracking in the presence of dense clutter. The unknown length of a stick-shape object is estimated in addition to the kinematic parameters. The kernel density estimation technique is utilised to approximate the joint posterior density of target state and static size parameters. The convolution particle filtering approach is validated on a Poisson model for the measurements, originating from the target and clutter. Examples illustrating the filter performance are presented. Simulation results show that the convolution particle filter provides accurate on-line tracking, with very good estimates both for the target kinematic states and for the parameters of the target extent.

    Item Type: Contribution in Book/Report/Proceedings
    Departments: Faculty of Science and Technology > School of Computing & Communications
    ID Code: 61540
    Deposited By: ep_importer_pure
    Deposited On: 08 Jan 2013 13:37
    Refereed?: No
    Published?: Published
    Last Modified: 24 Jun 2018 01:13
    Identification Number:

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