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Proceedings Paper

Weighted interacting particle-based nonlinear filter
Author(s): David J. Ballantyne; Surrey Kim; Michael A. Kouritzin
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Paper Abstract

Particle-based nonlinear filters have proven to be effective and versatile methods for computing approximations to difficult filtering problems. We introduce a novel hybrid particle method, thought to possess an excellent compromise between the unadaptive nature of the weighted particle methods and the overly random resampling in classical interactive particle methods, and compare this new method to our previously introduced refining branching particle filter. Our experiments involve various fixed numbers of particles and compare computational efficiency of our new method to the incumbent. The hybrid method is demonstrated to outperform two previous particle filters on our simulated test problems. To highlight the flexibility of particle filters, we choose to test our methods on a rectangularly-constrained Markov signal that does not satisfy a typical stochastic equation but rather a Skorohod, local time formulation. Whereas normal diffusive behavior occurs in the interior of the rectangular domain, immediate reflections are enforced at the boundary. The test problems involve a fish signal with boundary reflections and is motivated by the fish farming industry.

Paper Details

Date Published: 31 July 2002
PDF: 12 pages
Proc. SPIE 4729, Signal Processing, Sensor Fusion, and Target Recognition XI, (31 July 2002); doi: 10.1117/12.477609
Show Author Affiliations
David J. Ballantyne, Univ. of Alberta (Canada)
Surrey Kim, Univ. of Alberta (Canada)
Michael A. Kouritzin, Univ. of Alberta (Canada)

Published in SPIE Proceedings Vol. 4729:
Signal Processing, Sensor Fusion, and Target Recognition XI
Ivan Kadar, Editor(s)

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