Noise Integral‐Based Sparse Bayesian Learning for DOA Estimation Using Grid Pruning and Adaptation

ABSTRACT Sparse Bayesian learning (SBL) is widely applied in direction‐of‐arrival (DOA) estimation. However, it is limited by complexity, grid mismatch and inappropriate initial values of noise. To address these problems, a DOA estimation algorithm using grid pruning and adaptation based on noise integral‐based sparse Bayesian learning (AGNISBL) method is proposed in this letter. To reduce complexity, grid pruning is introduced for expectation maximization (EM) framework. Furthermore, a novel adaptive‐grid method is proposed for solving grid mismatch. A noise integral‐based inference framework is used to improve the robustness of the sparse Bayesian method. Simulation results show that the performance of the proposed AGNISBL approaches CRB at high signal‐to‐noise ratios (SNR) and with lower time complexity compared to other methods.