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Density Trees for Efficient Nonlinear State Estimation

Eberhardt, Henning; Klumpp, Vesa; Hanebeck, Uwe D.


In this paper, a new class of nonlinear Bayesian estimators based on a special space partitioning structure, generalized Octrees, is presented. This structure minimizes memory and calculation overhead. It is used as a container framework for a set of node functions that approximate a density piecewise. All necessary operations are derived in a very general way in order to allow for a great variety of Bayesian estimators. The presented estimators are especially well suited for multi-modal nonlinear estimation problems. The running time performance of the resulting estimators is first analyzed theoretically and then backed by means of simulations. All operations have a linear running time in the number of tree nodes.

Volltext §
DOI: 10.5445/IR/1000035090
DOI: 10.1109/icif.2010.5712086
Zitationen: 8
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: 1000035090
Erschienen in Proceedings of the 13th International Conference on Information Fusion (Fusion 2010), Edinburgh, Unit
Verlag Institute of Electrical and Electronics Engineers (IEEE)
Seiten 8 S.
Nachgewiesen in Dimensions
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