Title :
Two-Dimensional Mixed Autoregressive Models for Space-Time Adaptive Processing
Author :
Abramovich, Yuri I. ; Johnson, Ben A. ; Spencer, Nicholas K.
Author_Institution :
DSTO, Edinburgh
Abstract :
We introduce a new class of parametric models for two-dimensional (space-time) adaptive processing for (slow-time) stationary multivariate interference (clutter). This class is based on maximum-entropy (ME) extensions (completions) of partially specified block- Toeplitz covariance matrices. We derive exact solutions for the ME extensions and also provide computationally advantageous suboptimal solutions for efficient STAP filter design. The efficiency of the proposed parametric models is illustrated by an airborne radar scenario provided by the DARPA KASSPER dataset.
Keywords :
Toeplitz matrices; autoregressive processes; clutter; covariance matrices; maximum entropy methods; space-time adaptive processing; Toeplitz covariance matrices; clutter; maximum-entropy extensions; parametric models; space-time adaptive processing; stationary multivariate interference; two-dimensional mixed autoregressive models; Airborne radar; Antenna arrays; Australia; Clutter; Covariance matrix; Filters; Geometry; Linear antenna arrays; Parametric statistics; Solid modeling;
Conference_Titel :
Signals, Systems and Computers, 2007. ACSSC 2007. Conference Record of the Forty-First Asilomar Conference on
Conference_Location :
Pacific Grove, CA
Print_ISBN :
978-1-4244-2109-1
Electronic_ISBN :
1058-6393
DOI :
10.1109/ACSSC.2007.4487451
