Performance Analysis of Two-Dimensional Parametric STAP for Airborne Radar using KASSPER Data

We analyze the performance of a recently introduced class of two-dimensional (2-D) multivariate parametric models for space-time adaptive processing (STAP) in airborne radars on the DARPA airborne side-looking radar model known as KASSPER Dataset 1. Investigation of the impact of linear uniform antenna array errors on techniques that exploit spatial smoothing is demonstrated using a complementary phenomenological clutter model developed at the AFRL. Signal-to-interference-plus-noise ratio (SINR) degradation with respect to the optimal clairvoyant receiver is studied for different parametric models, antenna errors, and training sample volumes. We also analyze the impact of KASSPER training data inhomogeneity on STAP performance. For an extremely small number of training-data samples, we demonstrate that a properly selected parametric model and an accompanying covariance matrix estimation technique should achieve efficient performance for practical STAP applications.