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Dirac Mixture Density Approximation Based on Minimization of the Weighted Cramér-von Mises Distance

Schrempf, Oliver C.; Brunn, Dietrich; Hanebeck, Uwe D.


This paper proposes a systematic procedure for approximating arbitrary probability density functions by means of Dirac mixtures. For that purpose, a distance measure is required, which is in general not well defined for Dirac mixture densities. Hence, a distance measure comparing the corresponding cumulative distribution functions is employed. Here, we focus on the weighted Cramer-von Mises distance, a weighted integral quadratic distance measure, which is simple and intuitive. Since a closed-form solution of the given optimization problem is not possible in general, an efficient solution procedure based on a homotopy continuation approach is proposed. Compared to a standard particle approximation, the proposed procedure ensures an optimal approximation with respect to a given distance measure. Although useful in their own respect, the results also provide the basis for a recursive nonlinear filtering mechanism as an alternative to the popular particle filters

Volltext §
DOI: 10.5445/IR/1000013897
DOI: 10.1109/MFI.2006.265624
Zitationen: 20
Cover der Publikation
Zugehörige Institution(en) am KIT Fakultät für Informatik – Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2006
Sprache Englisch
Identifikator ISBN: 1-424-40566-1
KITopen-ID: 1000013897
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 512 - 517
Nachgewiesen in Dimensions
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