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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.

DOI: 10.1007/978-3-540-85640-5-22
Zugehörige Institution(en) am KIT Fakultät für Informatik – Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 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-Verlag
Seiten 287-300
Serie Lecture Notes in Electrical Engineering ; 24
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