A Probabilistic Approach to Multivariable Robust
Filtering, Prediction and Smoothing
32nd IEEE Conference on Decision and Control,
San Antonio, TX,
December 15-17, 1993, pp 1227-1232.
© 1993 IEEE.
Paper In Pdf 461K.
The performance of Wiener filters is degraded by the presence of
model errors. The paper
develops a method for robust design
of multisignal estimators. It is based on probabilistic descriptions
of the uncertainty and on the minimization of averaged
mean square error criteria.
A new approach to robust filtering, prediction and smoothing
of discrete-time signal vectors is presented.
Linear time-invariant filters are designed to be
insensitive to spectral uncertainty in signal
The goal is to obtain a simple design method,
leading to filters which are not overly conservative.
Modelling errors are described by sets of models, parametrized
by random variables with known covariances.
A robust design is obtained
by minimizing the H-2-norm of
the estimation error, averaged
with respect to the assumed model errors.
A polynomial solution, based on an
averaged spectral factorization and a unilateral Diophantine equation,
is presented. The robust
estimator is referred to as a cautious Wiener filter. It
turns out to be only slightly more
complicated to design than an ordinary Wiener filter.
- Related publications:
by Kenth Öhrn, May 1996, which considers also general
matrix fraction descriptions.
Paper in IEEE Trans. AC 1995,
including proofs and the dual feedforward control problem.
and comparison to minimax H-2, European Control Conf. 1995.
filtering, feedforward control
and uncertainty modelling, Automatica 1993.
Robust decision feedback equalizers,
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