Difference between revisions of "NCS: Moving Horizon Estimation"
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In this lecture, we give an introduction to moving horizon estimation (MHE) and extended Kalman filters (EKF). These filter stuctures can be used with nonlinear models and are therefore more general than the standard Kalman filter. Furthermore, MHE can also take constraints on the noise and the state space into account, and deal with asymmetric probability distributions. | |||
== Lecture Materials == | == Lecture Materials == | ||
Revision as of 16:51, 19 April 2006
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In this lecture, we give an introduction to moving horizon estimation (MHE) and extended Kalman filters (EKF). These filter stuctures can be used with nonlinear models and are therefore more general than the standard Kalman filter. Furthermore, MHE can also take constraints on the noise and the state space into account, and deal with asymmetric probability distributions.
