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The Sliced Gaussian Mixture Filter for Efficient Nonlinear Estimation

Klumpp, Vesa 1; Sawo, Felix 1; Hanebeck, Uwe D. 1; Fränken, Dietrich
1 Universität Karlsruhe (TH)

Abstract:
This paper addresses the efficient state estimation for mixed linear/nonlinear dynamic systems with noisy measurements. Based on a novel density representation - sliced Gaussian mixture density - the decomposition into a (conditionally) linear and nonlinear estimation problem is derived. The systematic approximation procedure minimizing a certain distance measure allows the derivation of (close to) optimal and deterministic estimation results. This leads to high-quality representations of the measurement-conditioned density of the states and, hence, to an overall more efficient estimation process. The performance of the proposed estimator is compared to state-of-the-art estimators, like the well-known marginalized particle filter.


Volltext §
DOI: 10.5445/IR/1000034861
Scopus
Zitationen: 14
Cover der Publikation
Zugehörige Institution(en) am KIT Fakultät für Informatik – Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2008
Sprache Englisch
Identifikator ISBN: 978-3-8007-3092-6
urn:nbn:de:swb:90-348615
KITopen-ID: 1000034861
Erschienen in Proceedings of the 11th International Conference on Information Fusion (Fusion 2008), Cologne, Germany, June 30 2008-July 3 2008
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 1-8
Nachgewiesen in Scopus
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