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Paper F. Adaptive Gaussian Mixture Filter Based on Statistical Linearization. Edited version of the paper: M. F. Huber. Adaptive Gaussian Mixture Filter Based on Statistical Linearization. In Proceedings of the 14th International Conference on Information Fusion (Fusion), Chicago, Illinois, July 2011

Huber, Marco F.

Abstract:
Gaussian mixtures are a common density representation in nonlinear, non-Gaussian Bayesian state estimation. Selecting an appropriate number of Gaussian components, however, is difficult as one has to trade of computational complexity against estimation accuracy. In this paper, an adaptive Gaussian mixture filter based on statistical linearization is proposed. Depending on the nonlinearity of the considered estimation problem, this filter dynamically increases the number of components via splitting. For this purpose, a measure is introduced that allows for quantifying the locally induced linearization error at each Gaussian mixture component. The deviation between the nonlinear and the linearized state space model is evaluated for determining the splitting direction. The proposed approach is not restricted to a specific statistical linearizationmethod. Simulations show the superior estimation performance compared to related approaches and common filtering algorithms.


Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Buchaufsatz
Jahr 2015
Sprache Englisch
Identifikator URN: urn:nbn:de:swb:90-460677
KITopen-ID: 1000046067
Erschienen in Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications. Ed.: M. Huber
Verlag KIT, Karlsruhe
Seiten 334-358
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