Two-Dimensional Mixed Autoregressive Models for Space-Time Adaptive Processing
Date
2007
Authors
Abramovich, Y.
Johnson, B.A.
Spencer, N.
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
Citation
Conference record / Asilomar Conference on Signals, Systems & Computers. Asilomar Conference on Signals, Systems & Computers, 2007, pp.1367-1371
Statement of Responsibility
Conference Name
41st Asilomar Conference on Signals, Systems & Computers (4 Nov 2007 - 7 Nov 2007 : Pacific Grove)
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.