State-Space Estimation with Uncertain Models

Ali H. Sayed () and Ananth Subramanian ()
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Ali H. Sayed: University of California, Adaptive Systems Laboratory, Electrical Engineering Department
Ananth Subramanian: University of California, Adaptive Systems Laboratory, Electrical Engineering Department

A chapter in Total Least Squares and Errors-in-Variables Modeling, 2002, pp 191-202 from Springer

Abstract: Abstract There are always discrepancies between design models and the actual physical systems or phenomena that they model. Regardless of their source, such perturbations can degrade the performance of otherwise optimal designs. This article discusses a design strategy for models with bounded perturbations. In comparison to other robust formulations, the resulting procedure performs data regularization as opposed to de-regularization. Applications in state-space estimation and adaptive filtering are discussed.

Keywords: regularization; least-squares; robust filter; adaptive filter; Kalman filter; parametric uncertainty. (search for similar items in EconPapers)
Date: 2002
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-94-017-3552-0_17

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DOI: 10.1007/978-94-017-3552-0_17

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