Signal Processing Filters Under Modeling Uncertainties.
S.A. Kassam, Tong Leong Lim, Leonard J. Cimini · 1979
Matched and Wiener filters are considered for signal processing applications when the a priori information about signal and noise characteristics are not completely specified. The approach is to design filters which are saddle-point or max-min solutions for the criterion functional (mean-squared-error or signal-to-noise ratio) over the classes of allowable signal shapes and signal and noise spectral densities. Two-dimensional discrete-parameter processes are considered, and some numerical examples are presented. (Author)