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URN: urn:nbn:de:swb:90-351273
Originalveröffentlichung
DOI: 10.3182/20110828-6-IT-1002.03359

(Semi-)Analytic Gaussian Mixture Filter

Huber, Marco F.; Beutler, Frederik; Hanebeck, Uwe D.

Abstract:
In nonlinear filtering, special types of Gaussian mixture filters are a straightforward extension of Gaussian filters, where linearizing the system model is performed individually for each Gaussian component. In this paper, two novel types of linearization are combined with Gaussian mixture filters. The first linearization is called analytic stochastic linearization, where the linearization is performed analytically and exactly, i.e., without Taylor-series expansion or approximate sample-based density representation. In cases where a full analytical linearization is not possible, the second approach decomposes the nonlinear system into a set of nonlinear subsystems that are conditionally integrable in closed form. These approaches are more accurate than fully applying classical linearization.


Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Jahr 2011
Sprache Englisch
Identifikator ISBN: 978-3-902661-93-7

KITopen-ID: 1000035127
Erschienen in Proceedings of the 18th IFAC World Congress (IFAC 2011), Milan, Italy, August 28 - September 2, 2011. Pt. 1. Ed.: S. Bittanti
Verlag IEEE, Piscataway
Seiten 1014-1020
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