An RKHS approach to robust L/sup 2/ estimation and signal detection

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. >