Hybrid Multi-Class Token Vision Transformer Convolutional Network for DOA Estimation

In this letter, we propose an efficient hybrid model, named HMC-ViT, that combines a convolutional neural network (CNN) with a multi-class token vision transformer (ViT) to address the problem of direction of arrival (DOA) estimation. HMC-ViT integrates the local feature extraction capability of CNN with the global feature extraction capability of ViT to enhance DOA estimation performance and improve the computational efficiency of ViT. Additionally, the ViT component employs multiple class tokens in parallel to generate spatial spectra for sub-regions, further enhancing the model's performance. Simulation results demonstrate that the proposed method outperforms existing approaches under low signal-to-noise ratio (SNR) scenarios. The code of this letter is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/morigen0721/HMC-ViT</uri>.