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Zbl 1009.93075
Sayed, Ali H.
A framework for state-space estimation with uncertain models.
(English)
[J] IEEE Trans. Autom. Control 46, No.7, 998-1013 (2001). ISSN 0018-9286

The paper develops a framework for state-space estimation when the parameters of the underlying linear model are subject to uncertainties. The proposed filters are designed to minimize the worst possible regularized residual norm over the class of admissible uncertainties. The author focuses on the following uncertain state-space model $$x_{i+1}= (F_i+\delta F_i)x_i+ G_i u_i,\quad y_i= H_i x_i+ v_i,$$ which is often studied in the literature on robust filtering. Simulation results and comparisons with existing robust filters are provided.
[Grigori Milstein (Berlin)]
MSC 2000:
*93E10 Estimation and detection in stochastic control
93E11 Filtering in stochastic control

Keywords: parametric uncertainty; quadratic stability; state-space estimation; regularized residual norm; robust filtering

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