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Recursive Prediction of Stochastic Nonlinear Systems Based on Optimal Dirac Mixture Approximations

Schrempf, Oliver C.; Hanebeck, Uwe D.


This paper introduces a new approach to the recursive propagation of probability density functions through discrete-time stochastic nonlinear dynamic systems. An efficient recursive procedure is proposed that is based on the optimal approximation of the posterior densities after each prediction step by means of Dirac mixtures. The parameters of the individual components are selected by systematically minimizing a suitable distance measure in such a way that the future evolution of the approximate densities is as close to the exact densities as possible.

Volltext §
DOI: 10.5445/IR/1000034830
DOI: 10.1109/ACC.2007.4282938
Zitationen: 14
Cover der Publikation
Zugehörige Institution(en) am KIT Fakultät für Informatik – Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2007
Sprache Englisch
Identifikator ISBN: 1-4244-0988-8
KITopen-ID: 1000034830
Erschienen in Proceedings of the 2007 American Control Conference (ACC 2007), New York, NY, USA, July, 2007
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 1768-1774
Nachgewiesen in Dimensions
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