A Single Snapshot DOA Estimation Method Based on ADMM-Net

Direction of arrival (DOA) can be estimated through sparse recovery (SR) methods based on the sparsity of signals. However, conventional SR-DOA methods, such as the alternating direction method of multipliers (ADMM), encounter issues such as difficulty in parameter setting and insufficient estimation accuracy. ADMM-Net is a neural network that combines ADMM with deep unfolding (DU). In this letter, we propose a method for estimating DOA using ADMM-Net. The simulation results demonstrate that the proposed method achieves a better balance between DOA estimation performance and real-time constraints.