Two-Dimensional World Decomposition Based Parametric Methods for Space-Time Adaptive Processing of Radar Signals
Abstract
Generalized multivariate analysis of variance and related reduced-rank regression are general statistical models that comprise versions of regression, canonical correlation, and analyses, as well as analysis of variance (ANOVA) and covariance in univariate and multivariate settings. It is a powerful and yet not very well known tool. In 1 we develop a unified framework for explaining, analyzing, and extending signal processing methods based on GMANOVA. We show the applicability of this framework to a number of detection and estimation problems in signal processing and communications, and provide new and simple ways to derive numerous existing algorithms for: 1) radar target estimation and detection; 2) source location using parametric signal models; 3) synchronization and space-time channel and noise estimation; 4) space-time symbol detection in 5) blind channel equalization, estimation, and signal separation; 6) spectral analysis nuclear magnetic resonance (NMR) spectroscopy. Many of the above methods were originally derived "from scratch," without knowledge of their close relationship with the GMANOVA model. We explicitly show this relationship and present new insights and guidelines for generalizing these methods. We also acknowledge the pioneering works of Brillinger (on frequency-wave number analysis, and Kelly and Forsythe (on radar detection, who first applied GMANOVA to signal processing problems. Our results could inspire applications of the general framework of GMANOVA to new problems in signal processing. We present such an application to flaw detection in nondestructive evaluation (NDE) of materials. A promising area for future growth is image processing.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 2004
- Accession Number
- ADA427376
Entities
People
- Arye Nehorai
Organizations
- University of Illinois at Chicago