Direction‐of‐Arrival Estimation Using Deep Learning With Covariance Matrix Reconstruction Under Limited Snapshots

Under low‐snapshot conditions, traditional direction‐of‐arrival (DOA) estimation suffers from covariance instability, while existing deep learning methods rely on complex architectures. This letter proposes a hybrid approach that combines model‐driven and data‐driven theories to strike a balance between estimation performance and computational cost. We reconstruct a structured covariance matrix by applying adaptive diagonal loading. The reconstructed matrix is then transformed into a two‐channel input and fed into the proposed squeeze‐and‐excitation multi‐scale deep convolutional network (SE‐MSDCN). DOA estimates are obtained via a sub‐grid peak interpolation strategy. The experimental results and our analysis validate the efficiency and superiority of the proposed method.