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

Schrempf, Oliver C.; Hanebeck, Uwe D.

Abstract:
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.


Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Jahr 2007
Sprache Englisch
Identifikator ISBN: 1-4244-0988-8
URN: urn:nbn:de:swb:90-348304
KITopen ID: 1000034830
Erschienen in Proceedings of the 2007 American Control Conference (ACC 2007), New York, NY, USA, July, 2007
Verlag IEEE, Piscatway
Seiten 1768-1774
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