Analysis of a separable STAP algorithm for very large arrays

Studies of massive MIMO in wireless communications have recently attracted significant attention. Here we study the benefits of very large arrays in space-time adaptive processing (STAP) for radar by analyzing the performance of a reduced-dimension separable STAP algorithm that exploits the large-array assumption. In particular, we begin by studying the behavior of the algorithm for clairvoyant interference covariance matrices with orthogonality assumptions on the steering vectors, and show that in the asymptotic sense this simplified scheme performs as well as the fully adaptive STAP method. We then appeal to random matrix theory to analyze performance when the covariance matrix is estimated using secondary data.