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Conference paper

A Computationally Efficient and Robust Implementation of the Continuous-Discrete Extended Kalman Filter

From

Scientific Computing, Department of Informatics and Mathematical Modeling, Technical University of Denmark1

Department of Informatics and Mathematical Modeling, Technical University of Denmark2

Mathematical Statistics, Department of Informatics and Mathematical Modeling, Technical University of Denmark3

Department of Chemical and Biochemical Engineering, Technical University of Denmark4

We present a novel numerically robust and computationally efficient extended Kalman filter for state estimation in nonlinear continuous-discrete stochastic systems. The resulting differential equations for the mean-covariance evolution of the nonlinear stochastic continuous-discrete time systems are solved efficiently using an ESDIRK integrator with sensitivity analysis capabilities.

This ESDIRK integrator for the mean- covariance evolution is implemented as part of an extended Kalman filter and tested on a PDE system. For moderate to large sized systems, the ESDIRK based extended Kalman filter for nonlinear stochastic continuous-discrete time systems is more than two orders of magnitude faster than a conventional implementation.

This is of significance in nonlinear model predictive control applications, statistical process monitoring as well as grey-box modelling of systems described by stochastic differential equations.

Language: English
Publisher: IEEE
Year: 2007
Pages: 3706-3712
Proceedings: American Control Conference 2007
ISBN: 1424409888 , 1424409896 , 9781424409884 and 9781424409891
ISSN: 23785861 and 07431619
Types: Conference paper
DOI: 10.1109/ACC.2007.4282549
ORCIDs: Jørgensen, John Bagterp and Madsen, Henrik

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