State Estimation Using a Reduced-Order Kalman Filter

@inproceedings{Farrell2001StateEU,
  title={State Estimation Using a Reduced-Order Kalman Filter},
  author={B F Farrell and Petros J. Ioannou},
  year={2001}
}
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… CONTINUE READING

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