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
Full text not available from this repository.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: | 19 Sep 2012 12:37 |
| Identification Number: | |
| URI: | http://eprints.lancs.ac.uk/id/eprint/49439 |
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