Direction-of-Arrival Estimation with Discrete Fourier Transform and Deep Feature Fusion

High-precision Direction-of-Arrival (DOA) estimation leveraging multi-sensor array architectures represents a frontier research domain in advanced array signal processing systems. Compared to traditional model-driven estimation methods like MUSIC and ESPRIT, data-driven approaches offer advantages such as higher estimation accuracy and simpler structures. Convolutional neural networks (CNNs) currently dominate deep learning approaches for DOA estimation. However, traditional CNNs suffer from limitations in capturing global features of covariance matrices due to their restricted local receptive fields, alongside challenges such as noise sensitivity and poor interpretability. To address these issues, we propose a novel Discrete Fourier Transform (DFT)-based deep learning framework for DOA estimation called DFNeT, leveraging the advantages of Fourier transform-enhanced networks in global modeling, computational efficiency, and noise robustness. Specifically, our approach introduces a DFT-based deep feature fusion network to denoise covariance matrices by integrating spatial and frequency-domain information. Subsequently, a series of DFT modules are designed to extract discriminative frequency-domain features, enabling accurate and robust DOA estimation. This method effectively mitigates noise interference while enhancing the interpretability of feature extraction through explicit frequency-domain operations. The simulation results demonstrate the effectiveness of the proposed method.