State Estimation Using a Reduced-Order Kalman Filter

Abstract Minimizing forecast error requires accurately specifying the initial state from which the forecast is made by optimally using available observing resources to obtain the most accurate possible analysis. The Kalman filter accomplishes this for a wide class of linear systems, and experience shows that the extended Kalman filter also performs well in nonlinear systems. Unfortunately, the Kalman filter and the extended Kalman filter require computation of the time-dependent error covariance matrix, which presents a daunting computational burden. However, the dynamically relevant dimension of the forecast error system is generally far smaller than the full state dimension of the forecast model, which suggests the use of reduced-order error models to obtain near-optimal state estimators. A method is described and illustrated for implementing a Kalman filter on a reduced-order approximation of the forecast error system. This reduced-order system is obtained by balanced truncation of the Hankel operator ...

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