Adaptive Support-Driven Sparse Recovery STAP Method with Subspace Penalty

Abstract

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

Beam-Space Post-Doppler Reduced-Dimension STAP Based on Sparse Bayesian Learning

“…In order to verify the robustness, we introduce the gain-phase (GP) error in the simulation experiments in Figure 11. According to [16,24], the clutter model with GP errors can be expressed as…”