The application of reproducing kernel Hilbert space (RKHS) 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 L2 estimation, matched filtering, and quadratic detection in the presence of uncertainties regarding the second-order structure of the random processes involved.
All Science Journal Classification (ASJC) codes
- Information Systems
- Computer Science Applications
- Library and Information Sciences