Two-Dimensional Mixed Autoregressive Models for Space-Time Adaptive Processing

Author

Abramovich, Yuri I. ; Johnson, Ben A. ; Spencer, Nicholas K.

Author_Institution

DSTO, Edinburgh

fYear

2007

fDate

4-7 Nov. 2007

Firstpage

1367

Lastpage

1371

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 2007. ACSSC 2007. Conference Record of the Forty-First Asilomar Conference on

Conference_Location

Pacific Grove, CA

ISSN

1058-6393

Print_ISBN

978-1-4244-2109-1

Electronic_ISBN

1058-6393

Type

conf

DOI

10.1109/ACSSC.2007.4487451

Filename

4487451