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A state estimator for nonlinear stochastic systems based on dirac mixture approximations

Schrempf, Oliver C.; Hanebeck, Uwe D.

This paper presents a filter approach for estimating the state of nonlinear dynamic systems based on recursive approximation of posterior densities by means of Dirac mixture functions. The filter consists of a prediction step and a filter step. The approximation approach is based on a systematic minimization of a distance measure and is hence optimal and deterministic. In contrast to non-deterministic methods we are able to determine the optimal number of components in the Dirac mixture. A further benefit of the proposed approach is the consideration of measurements during the approximation process in order to avoid parameter degradation.

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Volltext §
DOI: 10.5445/IR/1000034838
Seitenaufrufe: 21
seit 30.05.2018
Downloads: 69
seit 11.05.2013
Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Jahr 2007
Sprache Englisch
Identifikator ISBN: 978-972-8865-84-9
KITopen-ID: 1000034838
Erschienen in Proceedings of the 4th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2007), Angers, France, May, 2007. Vol. SPSMC. Ed.: J. Zaytoon
Verlag INSTICC Press, Setúbal
Seiten 54-61
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