F. Wang and V. Balakrishnan
In Proc. IEEE American Control Conf.,
pages 440-444,
San Diego, California,
June 1999
Abstract:
We present an adaptive robust Kalman filtering algorithm
that addresses estimation problems that arise in linear
time-varying systems with stochastic parametric
uncertainties. The filter has the one-step
predictor-corrector structure and minimizes the mean
square estimation error at each step, with the
minimization reduced to a convex optimization problem
based on linear matrix inequalities. The algorithm is
shown to converge when the system is mean square stable
and the state-space matrices are time-invariant. A
numerical example, consisting of equalizer design for a
communication channel, demonstrates that our algorithm
offers considerable improvement in performance when
compared to standard Kalman filtering techniques.
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