Ieee Transactions on Signal Processing 51,10 (2003) 2550-2558;
For uncertain systems containing both deterministic and stochastic uncertainties, we consider two problems of optimal filtering. The first is the design of a linear time-invariant filter that minimizes an upper bound on the mean energy gain between the noise affecting the system and the estimation error. The second is the design of a linear time-invariiint filter that minimizes an up per bound on the asymptotic mean square estimation error when the plant is driven by a white noise. We present filtering algorithms that solve each of these problems, with the filter parameters determined via convex optimization based on linear matrix inequalities. We demonstrate the performance of these robust algorithins on a numerical example consisting of the design of equalizers for a communication channel.
linear matrix inequality;; parametric uncertainty;; robust filtering;; discrete-time-systems;; model;; noise
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