Brief paper: An analysis of the effects of spectral uncertainty on wiener filtering

Authors:
Kenneth S. Vastola;H.Vincent Poor
Affiliations:
Department of Electrical Engineering and Computer Science, Princeton University, Princeton, NJ 08544, U.S.A.;Department of Electrical Engineering, Coordinated Science Laboratory, University of Illinois, Urbana, IL 61801, U.S.A.
Venue:
Automatica (Journal of IFAC)
Year:
1983

Citing 4
Cited 1

An alternative approach to the linear causal least-square filtering theory

IEEE Transactions on Information Theory
Robust detection of known signals

IEEE Transactions on Information Theory
Robust sequential detection of signals in noise

IEEE Transactions on Information Theory
Robust hypothesis testing for bounded classes of probability densities (Corresp.)

IEEE Transactions on Information Theory

Classifier design given an uncertainty class of feature distributions via regularized maximum likelihood and the incorporation of biological pathway knowledge in steady-state phenotype classification

Pattern Recognition

Quantified Score

Hi-index	22.14

Visualization

Abstract

A representative selection of results from an extensive study of the performance of Wiener filtering under spectral uncertainty is presented. For a variety of spectral uncertainty models the Wiener filter is shown to have undesirable sensitivity to even small deviations from those signal and noise spectral densities which were used to design the filter. Performance of a robust filter (designed to have the best possible performance when the uncertainty is worst) is also examined. In most cases the robust filter's insensitivity to spectral uncertainty makes it preferable to the traditional Wiener filter.