Marginalized Particle Filters for Bayesian Estimation of Gaussian Noise Parameters

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F10%3A00347241" target="_blank" >RIV/67985556:_____/10:00347241 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Marginalized Particle Filters for Bayesian Estimation of Gaussian Noise Parameters
Original language description
The particle ?lter provides a general solution to the nonlinear ?ltering 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 ?lter 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 vectorwith sensor o?sets 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 ?lter is applied to and illustrated with a standard example from literature.
Czech name
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Czech description
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Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 13th International Conference on Information Fusion
ISBN
978-0-9824438-1-1
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
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Publisher name
IET
Place of publication
Edinburgh
Event location
Edinburgh
Event date
Jul 26, 2010
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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