Block sparse Bayesian learning for broadband bearing estimation in shallow-water waveguides

For horizontal arrays in shallow-water waveguides, methods based on the plane wave assumption often produce biased bearing estimates because of multimode propagation. Although subspace-intersection methods reduce bias, they generally require a large array aperture to achieve satisfactory performance. This letter proposes a broadband bearing estimation method based on block sparse Bayesian learning. The proposed method groups modal steering vectors into a block-structured dictionary and exploits joint block sparsity across frequencies to estimate the bearing while mitigating estimation bias. Simulation and experimental results demonstrated that the proposed method provides sharper bearing peaks and better additive noise robustness than the comparison methods.