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URN: urn:nbn:de:swb:90-350857

Nonlinear Bayesian Estimation with Compactly Supported Wavelets

Hekler, Achim; Kiefel, Martin; Hanebeck, Uwe D.

Abstract:
Bayesian estimation for nonlinear systems is still a challenging problem, as in general the type of the true probability density changes and the complexity increases over time. Hence, approximations of the occurring equations and/or of the underlying probability density functions are inevitable. In this paper, we propose an approximation of the conditional densities by wavelet expansions. This kind of representation allows a sparse set of characterizing coefficients, especially for smooth or piecewise smooth density functions. Besides its good approximation properties, fast algorithms operating on sparse vectors are applicable and thus, a good trade-off between approximation quality and run-time can be achieved. Moreover, due to its highly generic nature, it can be applied to a large class of nonlinear systems with a high modeling accuracy. In particular, the noise acting upon the system can be modeled by an arbitrary probability distribution and can influence the system in any way.


Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Jahr 2010
Sprache Englisch
Identifikator ISBN: 978-1-4244-7745-6

KITopen-ID: 1000035085
Erschienen in Proceedings of the 2010 IEEE Conference on Decision and Control (CDC 2010), Atlanta, Georgia, USA, 15-17 Dec. 2010
Verlag IEEE, Piscataway
Seiten 5701-5706
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