Work

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

Year: 2025

Type: article

Abstract: ABSTRACT 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.

Source: Electronics Letters

DOI: 10.1049/ell2.70373

Authors: Yonghong Zhao, Jisong Liu, Xiumei Fan, Hongbo Cao

Institution: Xi'an University of Technology

Language: English

Topic: Direction-of-Arrival Estimation Techniques

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,590

Funder: Education Department of Shaanxi Province