Abstract
In this letter, to improve the performance of the space-time adaptive processing (STAP) filter with finite training samples, a novel algorithm with multiple measurement vectors (MMV) based on sparse recovery (SR) is proposed. Compared with traditional SR STAP algorithms, we utilize the knowledge of Capon spectrum to design a weighted <inline-formula><tex-math notation="LaTeX">${\ell }_2{\rm{ - norm}}$</tex-math></inline-formula> penalty which can better approximate the original <inline-formula><tex-math notation="LaTeX">${\ell }_0{\rm{ - norm}}$</tex-math></inline-formula>. Besides, the proposed algorithm has fast convergence performance and closed-form analytic solution in each iteration. Simulation results demonstrate the effectiveness and great performance of the proposed method.
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