Novel graph neural network and GNN-C-Transformer model construction for direction of arrival estimation

Abstract

Direction of Arrival (DOA) estimation is essential in radar, sonar, wireless communications, and speech processing. Traditional methods like MUSIC and ESPRIT provide high resolution but suffer from high computational complexity and poor performance in low signal-to-noise ratio (SNR) environments. Recent advances in neural networks, particularly Convolutional Neural Networks (CNN), improve accuracy and robustness; however, CNNs’ ability to reduce time complexity and improving robustness under low SNR conditions remains insufficient.

This paper presents a novel framework for DOA estimation in sparse arrays based on Graph Neural Networks (GNN) and proposes an entirely new array-based graph connectivity structure. By modeling the array geometry as a graph, our GNN approach captures spatial relationships effectively, addressing the challenges of time complexity and low SNR. We further integrate Transformer layers to capture both spatial and temporal dependencies, enhancing the model’s performance. Experimental results demonstrate that, at SNRs $\le 5\mathrm{dB}$, our GNN-based framework and the GNN-C-Transformer model developed thereon achieve superior accuracy compared to existing methods, while exhibiting lower computational complexity than all other algorithms except ESPRIT. This work advances the application of GNN-based DOA estimation by providing a scalable solution for large-scale, multi-dimensional signal processing in both dense and sparse array configurations.