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

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.