A Novel Off-grid DOA Estimation Approach Using Sparse Bayesian Learning

Sparse signal reconstruction methods for direction of arrival (DOA) estimation, such as sparse Bayesian learning (SBL), face the challenge of grid mismatch. Several off-grid DOA estimation methods have been proposed, but their performance depends on the tradeoff between accuracy and computational workload. In order to enhance the efficiency and performance of off-grid DOA estimation, we propose a grid refinement off-grid method based on sparse Bayesian learning (GROGSBL). We improve the stability of the grid refinement process by incorporating the off-grid model. Additionally, an off-grid DOA extraction method is adopted to further improve the accuracy of estimation. Simulation and experimental results demonstrate that the proposed method significantly enhances DOA estimation performance and exhibits superior computational efficiency.