A Deep Learning Approach for Direction-of-Arrival Estimation in Low-SNR Environments

To address the significant performance degradation of traditional direction-of-arrival (DOA) estimation algorithms under low-signal-to-noise ratio (SNR) conditions, this paper proposes a deep learning-based super-resolution DOA estimation network (DSDE-Net). Unlike classification modeling constrained by angular discretization, this approach formulates the DOA estimation problem as a regression task, thereby effectively improving angular estimation accuracy. DSDE-Net extracts features from both the spatial and temporal dimensions of the signal. A residual denoising module is incorporated during spatial feature extraction to suppress noise interference in covariance features. Building upon this foundation, a dynamic gated feature fusion network is designed. By learning dimension-wise fusion weights, it achieves an adaptive weighted combination of spatiotemporal features, enabling the network to dynamically adjust the contribution of various features under different SNR conditions. Experimental results demonstrate that compared to traditional algorithms and existing deep learning methods, it exhibits the smallest estimation error and overall standard deviation, delivering superior estimation accuracy and robustness in low-SNR environments.