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Marginalized Particle Filters for Bayesian Estimation of Gaussian Noise Parameters
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SYSNO ASEP 0347241 Document Type C - Proceedings Paper (int. conf.) R&D Document Type Conference Paper Title Marginalized Particle Filters for Bayesian Estimation of Gaussian Noise Parameters Author(s) Saha, S. (SE)
Okzan, E. (SE)
Gustafsson, F. (SE)
Šmídl, Václav (UTIA-B) RID, ORCIDSource Title Proceedings of the 13th International Conference on Information Fusion. - Edinburgh : IET, 2010 - ISBN 978-0-9824438-1-1 Pages s. 1-8 Number of pages 8 s. Publication form www - www Action 13th International Conference on Information Fusion Event date 26.07.2010-29.07.2010 VEvent location Edinburgh Country GB - United Kingdom Event type WRD Language eng - English Country GB - United Kingdom Keywords marginalized particle filter ; unknown noise statistics ; bayesian conjugate prior Subject RIV BC - Control Systems Theory CEZ AV0Z10750506 - UTIA-B (2005-2011) Annotation The particle filter provides a general solution to the nonlinear filtering problem with arbitrarily accuracy. However, the curse of dimensionality prevents its application in cases where the state dimensionality is high. Further, estimation of stationary parameters is a known challenge in a particle filter framework. We suggest a marginalization approach for the case of unknown noise distribution parameters that avoid both aforementioned problem. First, the standard approach of augmenting the state vector with sensor offsets and scale factors is avoided, so the state dimension is not increased. Second, the mean and covariance of both process and measurement noises are represented with parametric distributions, whose statistics are updated adaptively and analytically using the concept of conjugate prior distributions. The resulting marginalized particle filter is applied to and illustrated with a standard example from literature. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2011
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