Performance of 2-D Mixed Autoregressive Models for Airborne Radar STAP: KASSPER-Aided Analysis

摘要

We analyze the performance of a recently described class of two-dimensional autoregressive parametric models for space-time adaptive processing (STAP) in airborne radars on the DARPA side-looking radar model known as KASSPER Dataset 1. We investigate the trade-offs between signal-to-interference-plus-noise ratio (SINR) degradation (with respect to the optimal clairvoyant receiver) due to the mismatch between the observed covariance matrix and its parametric model, and the degradation due to the limited training sample volume. The impact of ground-clutter inhomogeneity on parametric STAP performance is demonstrated, as well as the significant superiority of parametric STAP over the conventional loaded sample-matrix inversion (SMI) technique.