Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/28989
Title: Robust filtering under stochastic parametric uncertainties
Authors: Zhang, H
Zhang, D 
Xie, L
Lin, J
Issue Date: 2004
Source: Automatica, 2004, v. 40, no. 9, p. 1583-1589
Abstract: This paper is concerned with a polynomial approach to robust deconvolution filtering of linear discrete-time systems with random modeling uncertainties. The modeling errors appear in the coefficients of the numerators and denominators of both the input signal and system transfer function models in the form of random variables with zero means and known upper bounds of the covariances. The robust filtering problem is to find an estimator that minimizes the maximum mean square estimation error over the random parameter uncertainties and input and measurement noises. The key to our solution is to quantify the effect of the random parameter uncertainties by introducing two fictitious noises for which a simple way is given to calculate their covariances. The optimal robust estimator is then computed by solving one spectral factorization and one polynomial equation as in the standard optimal estimator design using a polynomial approach. An example of signal detection in mobile communication is given to illustrate the effectiveness of our approach.
Keywords: Polynomial approach
Robust estimation
Signal detection
Spectral factorization
Stochastic uncertainty
Publisher: Pergamon Press
Journal: Automatica 
ISSN: 0005-1098
DOI: 10.1016/j.automatica.2004.04.002
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