A Space-time Adaptive Processing Algorithm Based on Joint Sparse Recovery

Sparse recovery Space-Time Adaptive Processing (STAP) methods for obtaining the clutter spectrum require few training samples and can effectively suppress clutter in nonhomogeneous clutter environments. However, presently available sparse recovery STAP methods only use single training samples to recover the clutter spectrum, neglecting information from multiple samples. Moreover, the recovery performance of the abovementioned methods is sensitive to noise. In this study, a subspace-based jointly sparse recovery method is proposed. The information from multiple training samples is fully used and robust clutter suppression performance in noisy environments is achieved. Simulation results show the effectiveness of the proposed method.