Two-Dimensional Filters for Signal Processing under Modeling Uncertainties

Matched and Wiener filters are considered for signal processing applications when the a priori information about signal and noise characteristics is not completely specified. The approach is to design filters which are saddle-point or max-min solutions for the criterion functional (mean-square-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 a numerical example is presented.

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