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Gaussian Filtering using State Decomposition Methods

Beutler, Frederik 1; Huber, Marco F. 1; Hanebeck, Uwe D. 1
1 Karlsruher Institut für Technologie (KIT)


State estimation for nonlinear systems generally requires approximations of the system or the probability densities, as the occurring prediction and filtering equations cannot be solved in closed form. For instance, Linear Regression Kalman Filters like the Unscented Kalman Filter or the considered Gaussian Filter propagate a small set of sample points through the system to approximate the posterior mean and covariance matrix. To reduce the number of sample points, special structures of the system and measurement equation can be taken into account. In this paper, two principles of system decomposition are considered and applied to the Gaussian Filter. One principle exploits that only a part of the state vector is directly observed by the measurement. The second principle separates the system equations into linear and nonlinear parts in order to merely approximate the nonlinear part of the state. The benefits of both decompositions are demonstrated on a real-world example.

Volltext §
DOI: 10.5445/IR/1000034955
Zitationen: 25
Cover der Publikation
Zugehörige Institution(en) am KIT Fakultät für Informatik – Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2009
Sprache Englisch
Identifikator ISBN: 978-0-9824-4380-4
KITopen-ID: 1000034955
Erschienen in Proceedings of the 12th International Conference on Information Fusion (Fusion 2009), Seattle, Washington, 6-9 July 2009
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 579-586
Nachgewiesen in Scopus
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