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Proceedings Paper

Progressive Bayes: a new framework for nonlinear state estimation
Author(s): Uwe D. Hanebeck; Kai Briechle; Andreas Rauh
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Paper Abstract

This paper is concerned with recursively estimating the internal state of a nonlinear dynamic system by processing noisy measurements and the known system input. In the case of continuous states, an exact analytic representation of the probability density characterizing the estimate is generally too complex for recursive estimation or even impossible to obtain. Hence, it is replaced by a convenient type of approximate density characterized by a finite set of parameters. Of course, parameters are desired that systematically minimize a given measure of deviation between the (often unknown) exact density and its approximation, which in general leads to a complicated optimization problem. Here, a new framework for state estimation based on progressive processing is proposed. Rather than trying to solve the original problem, it is exactly converted into a corresponding system of explicit ordinary first-order differential equations. Solving this system over a finite "time" interval yields the desired optimal density parameters.

Paper Details

Date Published: 1 April 2003
PDF: 12 pages
Proc. SPIE 5099, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2003, (1 April 2003); doi: 10.1117/12.487806
Show Author Affiliations
Uwe D. Hanebeck, Univ. Karlsruhe (Germany)
Kai Briechle, Technische Univ. Muenchen (Germany)
Andreas Rauh, Univ. Ulm (Germany)

Published in SPIE Proceedings Vol. 5099:
Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2003
Belur V. Dasarathy, Editor(s)

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