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Efficient Nonlinear Bayesian Estimation based on Fourier Densities

Brunn, Dietrich; Sawo, Felix; Hanebeck, Uwe D.


Efficiently implementing nonlinear Bayesian estimators is still not a fully solved problem. For practical applications, a trade-off between estimation quality and demand on computational resources has to be found. In this paper, the use of nonnegative Fourier series, so-called Fourier densities, for Bayesian estimation is proposed. By using the absolute square of Fourier series for the density representation, it is ensured that the density stays nonnegative. Nonetheless, approximation of arbitrary probability density functions can be made by using the Fourier integral formula. An efficient bayesian estimator algorithm with constant complexity for nonnegative Fourier series is derived and demonstrated by means of an example.

Volltext §
DOI: 10.5445/IR/1000013895
DOI: 10.1109/MFI.2006.265642
Zitationen: 7
Cover der Publikation
Zugehörige Institution(en) am KIT Fakultät für Informatik – Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2006
Sprache Englisch
Identifikator ISBN: 1-4244-0567-X
KITopen-ID: 1000013895
Erschienen in Proceedings / 2006 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems, 3 - 4 Sept. 2006, Heidelberg, Germany
Verlag IEEE Service Center
Seiten 312 - 322
Nachgewiesen in Dimensions
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