Efficient Iterated Filtering

Publication: Research - peer-reviewConference abstract in proceedings – Annual report year: 2012

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Parameter estimation in general state space models is not trivial as the likelihood is unknown. We propose a recursive estimator for general state space models, and show that the estimates converge to the true parameters with probability one. The estimates are also asymptotically Cramer-Rao efficient. The proposed estimator is easy to implement as it only relies on non-linear filtering. This makes the framework flexible as it is easy to tune the implementation to achieve computational efficiency. This is done by using the approximation of the score function derived from the theory on Iterative Filtering as a building block within the recursive maximum likelihood estimator.
Original languageEnglish
TitleSystem Identification
Volume16
PublisherInternational Federation of Automatic Control
Publication date2012
Pages1785-1790
ISBN (print)978-3-902823-06-9
DOIs
StatePublished

Conference

Conference16th IFAC Symposium on System Identification
CountryBelgium
CityBrussels
Period11/07/1213/07/12
Internet addresshttp://www.sysid2012.org/
NameIFAC Proceedings Volumes (IFAC-PapersOnline)
CitationsWeb of Science® Times Cited: No match on DOI

Keywords

  • Approximation theory, Iterative methods, Maximum likelihood estimation, Parameter estimation, State space methods, Estimation
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