Robust SR-STAP algorithms in partly calibrated arrays for airborne radar

Abstract

Sparse recovery based space-time processing (SR-STAP) techniques are capable of achieving satisfactory clutter suppression and target detection performance, even with limited training samples. The majority of existing approaches are developed under the assumption of ideal uniform linear arrays, where no imperfections are present. In this study, we consider the robust STAP problem in the context of airborne partly calibrated array (PCA) composed of several well calibrated subarrays with unknown inter-subarray gain-phase and displacement errors. Initially, the space-time signal for airborne STAP radar based on PCAs is modeled. Subsequently, the authors propose two robust SR-STAP algorithms, considering both grid-based and gridless methods, leveraging the block-sparsity and low rank structural characteristics inherent in the signal model. Extensive numerical experiments have shown the significant performance benefits including small sample applicability and resilience to inter-subarray gain-phase and displacement errors.