Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 485-501 |
| Number of pages | 17 |
| Journal | IEEE Transactions on Information Theory |
| Volume | 36 |
| Issue number | 3 |
| DOIs | |
| State | Published - May 1990 |
| Externally published | Yes |
All Science Journal Classification (ASJC) codes
- Information Systems
- Computer Science Applications
- Library and Information Sciences