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 978-0470639054Full text not available from this repository.
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|
|Deposited On:||09 Aug 2011 00:34|
|Last Modified:||15 Jan 2017 00:08|
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