Abstract
The application of RKHS (reproducing kernel Hilbert space) theory to the problems of robust signal detection and estimation is investigated. It is shown that this approach provides a general and unified framework in which to analyze the problems of L/sup 2/ estimation, matched filtering, and quadratic detection in the presence of uncertainties regarding the second-order structure of the random processes involved. >
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