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Support-Vector Conditional Density Estimation for Nonlinear Filtering

Krauthausen, Peter; Huber, Marco F.; Hanebeck, Uwe D.

A non-parametric conditional density estimation algorithm for nonlinear stochastic dynamic systems is proposed. The contributions are a novel sup- port vector regression for estimating conditional den- sities, modeled by Gaussian mixture densities, and an algorithm based on cross-validation for automatically determining hyper-parameters for the regression. The conditional densities are employed with a modi?ed axis- aligned Gaussian mixture filter. The experimental va- lidation shows the high quality of the conditional densi- ties and good accuracy of the proposed filter.

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Volltext §
DOI: 10.5445/IR/1000035093
Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Jahr 2010
Sprache Englisch
Identifikator ISBN: 978-0-9824438-1-1
KITopen-ID: 1000035093
Erschienen in Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, United Kingdom, 26-29 July 2010
Verlag IEEE, Piscataway
Seiten 8 S.
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