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The Sliced Gaussian Mixture Filter with Adaptive State Decomposition Depending on Linearization Error

Klumpp, Vesa; Beutler, Frederik; Hanebeck, Uwe D.; Fränken, Dietrich


In this paper, a novel nonlinear/non-linear model decomposition for the Sliced Gaussian Mixture Filter is presented. Based on the level of nonlinearity of the model, the overall estimation problem is decomposed into a severely nonlinear and a slightly nonlinear part, which are processed by different estimation techniques. To further improve the efficiency of the estimator, an adaptive state decomposition algorithm is introduced that allows decomposition according to the linearization error for nonlinear system and measurement models. Simulations show that this approach has orders of magnitude less complexity compared to other state of the art estimators, while maintaining comparable estimation errors.

Volltext §
DOI: 10.5445/IR/1000035091
Cover der Publikation
Zugehörige Institution(en) am KIT Fakultät für Informatik – Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2010
Sprache Englisch
Identifikator ISBN: 978-0-9824438-1-1
KITopen-ID: 1000035091
Erschienen in Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, United Kingdom, 26-29 July 2010
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 8 S.
Nachgewiesen in Dimensions
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