Abstract
Non-Gaussian signals arise in a wide variety of applications, including sonar, digital communications, seismology, and radio astronomy. In this tutorial overview, a hierarchical approach to signal modeling and detector design for non-Gaussian signals is described. In addition to being of interest in applications, this problem serves as a paradigm within which most of the areas of active research in statistical signal processing arise. In particular, the methodologies of nonlinear signal processing, higher order statistical analysis, signal representations, and learning algorithms, all can be juxtaposed quite naturally in this framework.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1061-1095 |
| Number of pages | 35 |
| Journal | Proceedings of the IEEE |
| Volume | 82 |
| Issue number | 7 |
| DOIs | |
| State | Published - Jul 1 1994 |
| Externally published | Yes |
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
Fingerprint Dive into the research topics of 'Detection of non-Gaussian signals: a paradigm for modern statistical signal processing'. Together they form a unique fingerprint.
Cite this
Garth, L. M., & Poor, H. V. (1994). Detection of non-Gaussian signals: a paradigm for modern statistical signal processing. Proceedings of the IEEE, 82(7), 1061-1095. https://doi.org/10.1109/5.293163