Improved Sparse Bayesian Off-Grid Direction-of-Arrival Estimation Under Mixed Noise

To improve the robustness and computational efficiency of underwater direction-of-arrival (DOA) estimation in complex marine noise environments, this study proposes an improved sparse Bayesian off-grid DOA estimation method under mixed noise. Within a hierarchical Bayesian framework, temporal precision variables for non-Gaussian noise and spatial precision variables for nonuniform noise are introduced and jointly estimated to adaptively reduce the weights of impulsive snapshots and noise-contaminated array elements. A space-alternating variational inference scheme transforms the joint estimation of signal and noise parameters into low-dimensional analytical updates, thereby reducing the computational burden associated with high-dimensional posterior covariance matrix inversion. In addition, adaptive grid refinement locally densifies high-confidence spectral-peak regions, while a first-order Taylor expansion is employed for continuous off-grid compensation. Simulation results show that the proposed method achieves more stable spatial spectrum reconstruction under mixed-noise and grid-mismatch conditions, providing a favorable balance between estimation accuracy and computational complexity. Results from the SWellEx-96 sea-trial data further demonstrate its ability to stably track target bearings under practical array geometry and unknown oceanic interference. These results verify the effectiveness and applicability of the proposed method in both mixed-noise simulations and real shallow-water data.