Sparse Bayesian DOA Estimation Using Hierarchical Synthesis Lasso Priors for Off-Grid Signals.

Within the conventional sparse Bayesian learning (SBL) framework, only Gaussian scale mixtures have been adopted to model sparsity-inducing priors that guarantee the exact inverse recovery. In light of the relative scarcity of formal SBL tools in enforcing a proper sparsity profile of signal vectors, we explore the use of hierarchical synthesis lasso (HSL) priors for representing the same small su...