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

John Bagterp Jørgensen, Per Grove Thomsen, Henrik Madsen, Morten Rode Kristensen

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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.
Original languageEnglish
Title of host publicationAmerican Control Conference, 2007. ACC '07
Publication date2007
ISBN (Print)1-4244-0988-8
Publication statusPublished - 2007
EventAmerican Control Conference 2007 - New York City, United States
Duration: 11 Jul 200713 Jul 2007


ConferenceAmerican Control Conference 2007
Country/TerritoryUnited States
CityNew York City
Internet address

Bibliographical note

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