Abstract
A unifying framework of steady-state Kalman filtering, smoothing and prediction for descriptor systems is presented by using the innovation analysis method in the time domain. The descriptor Kalman estimators are presented on the basis of the autoregressive moving-average innovation model and white-noise estimators. The new algorithms of steady-state descriptor Kalman estimators gains are given. The solution of the Riccati equation is avoided. To ensure the asymptotic stability of descriptor Kalman estimators with respect to the initial values of innovation process, formulae for selecting their initial values are given. A simulation example shows the usefulness of the proposed results.
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