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Dirac Mixture Approximation for Nonlinear Stochastic Filtering

Schrempf, Oliver C.; Hanebeck, Uwe D.

This work presents a filter for estimating the state of nonlinear dynamic systems. It is based on optimal recursive approximation the state densities by means of Dirac mixture functions in order to allow for a closed form solution of the prediction and 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 2008
Sprache Englisch
Identifikator ISBN: 978-3-540-85639-9
KITopen-ID: 1000029739
Erschienen in Informatics in Control, Automation and Robotics -- Selected Papers from the International Conference on Informatics in Control, Automation and Robotics
Verlag Springer, Berlin
Seiten 287-300
Serie Lecture Notes in Electrical Engineering ; 24
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