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

Schrempf, Oliver C.; Hanebeck, Uwe D.

Abstract: 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.

Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Jahr 2007
Sprache Englisch
Identifikator ISBN: 978-972-8865-84-9
URN: urn:nbn:de:swb:90-348386
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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