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Due to its self-regularizing nature and its ability to quantify uncertainty,the Bayesian approach has achieved excellent recovery performance across a widerange of sparse signal recovery applications. However, most existing methodsare based on the real-value signal model, with the complex-value signal modelrarely considered. Motivated by the adaptive least absolute shrinkage andselection operator (LASSO) and the sparse Bayesian learning (SBL) framework, ahierarchical model with adaptive Laplace priors is proposed in this paper forrecovery of complex sparse signals. Moreover, the space alternating approach isintegrated into the algorithm to reduce the computational complexity of theproposed method. In experiments, the proposed algorithm is studied for complexGaussian random dictionaries and different types of complex signals. Theseexperiments show that the proposed algorithm offers better recovery performancefor different types of complex signals than state-of-the-art methods.

Space Alternating Variational Estimation Based Sparse Bayesian Learning for Complex-value Sparse Signal Recovery Using Adaptive Laplace Priors