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2011
Conference Paper
Title

(Semi-)analytic Gaussian mixture filter

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.
Author(s)
Huber, Marco F.
Beutler, Frederik
Hanebeck, Uwe D.
Mainwork
18th World Congress of the International Federation of Automatic Control 2011. Proceedings  
Conference
International Federation of Automatic Control (World Congress) 2011  
File(s)
Download (556.4 KB)
DOI
10.24406/publica-r-372341
10.3182/20110828-6-IT-1002.03359
Language
English
Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB  
Keyword(s)
  • state estimation

  • nonlinear system

  • stochastic system

  • decomposition method

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