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To improve the performance of the recently developed parameter-dependent sparse recovery (SR) space-time adaptive processing (STAP) algorithms in real-world applications, the authors propose a novel clutter suppression algorithm with multiple measurement vectors (MMVs) using sparse Bayesian learning (SBL) strategy. First, the necessary and sufficient condition for uniqueness of sparse solutions to the SR STAP with MMV is derived. Then the SBL STAP algorithm in MMV case is introduced, and the process for hyperparameters estimation via expectation maximisation is given. Finally, a computational complexity comparison with the existing algorithms and an analysis of the proposed algorithm are conducted. Results with both simulated and the Mountain-Top data demonstrate the fast convergence and good performance of the proposed algorithm.

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Two adaptive detectors for range-spread targets in non-Gaussian clutter SCIENCE CHINA Information Sciences 54, 386 (2011); Generalized adaptive subspace detector for range-Doppler spread target with high resolution radar SCIENCE CHINA Information Sciences 54, 172 (2011); A novel adaptive V-BLAST algorithm based on subspace tracking and Hermitian matrix perturbation theorem SCIENCE CHINA Information Sciences 55, 322 (2012); Improved eigenanalysis canceler based on data-independent clutter subspace estimation for space-time adaptive processing

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