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Approximate Nonlinear Bayesian Estimation Based on Lower and Upper Densities

Klumpp, Vesa; Brunn, Dietrich; Hanebeck, Uwe D.


Recursive calculation of the probability density function characterizing the state estimate of a nonlinear stochastic dynamic system in general cannot be performed exactly, since the type of the density changes with every processing step and the complexity increases. Hence, an approximation of the true density is required. Instead of using a single complicated approximating density, this paper is concerned with bounding the true density from below and from above by means of two simple densities. This provides a kind of guaranteed estimator with respect to the underlying true density, which requires a mechanism for ordering densities. Here, a partial ordering with respect to the cumulative distributions is employed. Based on this partial ordering, a modified Bayesian filter step is proposed, which recursively propagates lower and upper density bounds. A specific implementation for piecewise linear densities with finite support is used for demonstrating the performance of the new approach in simulations.

Volltext §
DOI: 10.5445/IR/1000013901
DOI: 10.1109/ICIF.2006.301682
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Fakultät für Informatik – Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2006
Sprache Englisch
Identifikator ISBN: 1-424-40953-5
KITopen-ID: 1000013901
Erschienen in Proceedings / 9th International Conference on Information Fusion, July 2006, Florence, Italy
Verlag IEEE Service Center
Seiten 1 - 8
Nachgewiesen in Dimensions
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