AUTHOR=Wang Yan , Xiao Jing , Cheng Xiao , Wei Qiang , Tang Ning TITLE=Underwater acoustic signal classification based on a spatial–temporal fusion neural network JOURNAL=Frontiers in Marine Science VOLUME=Volume 11 - 2024 YEAR=2024 URL=https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2024.1331717 DOI=10.3389/fmars.2024.1331717 ISSN=2296-7745 ABSTRACT=In the paper, a novel fusion network for automatic modulation classification (AMC) is proposed in underwater acoustic communication, which consists of Transformer and depth wise convolution (DWC) network. Transformer breaks the limitation of sequential signal input and establishes the connection between different modulations in the parallel manner. Its attention mechanism can improve the modulation recognition ability by focusing on the key information. DWC is regularly inserted in the Transformer network to constitute spatial-temporal structure, which can enhance the classification results at lower signal-to-noise ratios (SNRs). The proposed method can obtain more deep features of underwater acoustic signals. The experiment results achieve average 92.1% at -4dB ≤ SNR ≤ 0dB, which exceed other state-of-the-art neural networks.