Adaptive Support-Driven Sparse Recovery STAP Method with Subspace Penalty

Detecting a moving target is an attractive topic in many fields, such as remote sensing. Space-time adaptive processing (STAP) plays key role detecting targets strong clutter backgrounds for airborne early warning radar systems. However, STAP suffers serious suppression performance loss when the number of training samples insufficient due to inhomogeneous environment. In this article, efficient sparse recovery algorithm proposed. First, inspired by relationship between multiple Bayesian learning (M-SBL) and subspace-based hybrid greedy algorithms, new optimization objective function based on subspace penalty established. Second, closed-form solution each minimization step obtained through alternating algorithm, which can guarantee convergence algorithm. Finally, restart strategy used adaptively update support, reduces computational complexity. Simulation results show that proposed has excellent suppression, speed running time with samples.