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Particle Filtering Combined with Interval Methods for Tracking Applications

Gning, Amadou and Mihaylova, Lyudmila and Abdallah, Fahed and Ristic, Branko (2012) Particle Filtering Combined with Interval Methods for Tracking Applications. In: Integrated Tracking, Classification, and Sensor Management. John Wiley and Sons, New Jersey, pp. 43-74. ISBN 978-0470639054

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Abstract

This chapter presents a new approach combining the Bayesian framework with interval methods. When the system dynamics and measurement models have interval types of uncertainties, instead of point state estimates, guaranteed (interval) estimation is a promising approach. First, fundamental concepts from the interval analysis are introduced. Next, a Box Particle Filter (Box-PF) is presented and its theoretical derivation is given based on a mixture of uniform probability density functions. The efficiency of the Box-PF is significant compared with the generic sampling importance resampling particle Filter (SIR PF). With few particles the Box-PF can achieve the same estimation accuracy that the SIR PF achieves with thousands of particles. The performance of the proposed Box-PF is studied and results over examples both with simulated and real data are presented.

Item Type: Contribution in Book/Report/Proceedings
Uncontrolled Keywords: Sequential Bayesian Estimation, ; nonlinear estimation ; Box Particle filters ; tracking ; nonlinear filtering ; interval uncertainty
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: Faculty of Science and Technology > School of Computing & Communications
ID Code: 49439
Deposited By: ep_importer_pure
Deposited On: 09 Aug 2011 00:34
Refereed?: No
Published?: Published
Last Modified: 10 Apr 2014 00:36
Identification Number:
URI: http://eprints.lancs.ac.uk/id/eprint/49439

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