Off-Grid DOA Estimation Method Based on Sparse Bayesian Learning With Clustered Structural-Aware Prior Information

The clustered property among sparse weight vector is prevalent in applications such as direction of arrival (DOA) estimation. To address this, block structure has been utilized to capture the clustered information expressed by the continuity of nonzero elements in a sparse weight vector. However, many compressive sensing (CS)-based methods exploit inter-block sparsity while ignoring intra-block correlations. Sparse Bayesian learning (SBL)-based methods with parameterized distribution provide a flexible approach for handling block structures, but most of them assume known block partitioning. In addition, off-grid problem and multi-measurement vector (MMV) scenario are also common and challenging issues in CS-based methods. In this work, we fully utilize the potential clustered structure in the sparse solution of DOA estimation and derive a structure-aware SBL framework (SASBL) in MMV scenarios. The structure-aware matrix characterizes the correlation within each block, and the weight vector can describe the inter-block sparsity. A new learning rule for the weight vector is derived based on the Majorization-Minimization algorithm. Furthermore, the off-grid parameter is incorporated into the SASBL framework to obtain higher DOA estimation resolution. Simulation results demonstrate that the proposed SASBL outperforms subspace-based, typical SBL-based, off-grid SBL-based and gridless methods.