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.
|Title of host publication||American Control Conference, 2007. ACC '07|
|Publication status||Published - 2007|
|Event||American Control Conference 2007 - New York City, United States|
Duration: 11 Jul 2007 → 13 Jul 2007
|Conference||American Control Conference 2007|
|City||New York City|
|Period||11/07/2007 → 13/07/2007|