An effective gridless sparse recovery space-time adaptive algorithm for airborne radar with non-uniform linear arrays

In recent years, gridless sparse recovery based space–time adaptive processing (SR-STAP) algorithms have attracted extensive attention due to their excellent estimation performance even with grid mismatch. Among them, the SR-STAP algorithm based on atomic norm minimization (ANM) stands out as the most representative. However, most current gridless SR-STAP algorithms rely on the 2D Vandermonde structure of the space–time steering vector and are therefore restricted to uniform linear arrays (ULAs). In practice, it is essential to efficiently utilize gridless SR-STAP methods to non-uniform linear arrays (NLAs) with varying configurations. In this paper, we propose a fast gridless SR-STAP method based on ANM for NLAs with multiple measurement vectors (MMV), namely FNLAANM-STAP. Inspired by the array manifold separation technique, we reformulate the original spatial steering vector as the product of a Vandermonde vector and a sampling matrix, adapting it for NLAs without compromising efficiency. Then we develop an efficient iterative approach by utilizing the accelerated proximal gradient (APG) framework, which offers a low-complexity solution. Simulation results demonstrate that our proposed method outperforms in clutter suppression while requiring less computational complexity.