数字海洋与水下攻防2026，Vol.9Issue(3)：257-267,11.DOI:10.19838/j.issn.2096-5753.2026.03.003
基于双分支神经网络的水声通信调制识别方法
Underwater Acoustic Communication Modulation Recognition Based on Dual-Branch Neural Networks
摘要
Abstract
Modulation recognition of underwater acoustic communication signals is a key task in signal interception processing.In recent years,recognition methods combining time-frequency features with neural networks have attracted considerable attention.To address the insufficient generalization ability of conventional modulation recognition methods under cross-noise-distribution conditions and the confusion between similar modulation classes caused by single-feature representation,an underwater acoustic communication modulation recognition method based on a dual-branch neural network is proposed in this paper.Taking the raw signal waveform as input,the proposed method constructs a pre-trained audio encoder branch and a spectral feature branch,and further designs an adaptive decision fusion strategy guided by static fusion to jointly optimize the decisions of the two branches.Experimental results show that,under test conditions involving unseen noise types,the proposed method improves the average recognition accuracy by 13%compared with the best-performing traditional spectrogram-based method.Meanwhile,the discrimination ability for similar modulation classes is enhanced,with the proportion of QPSK samples misclassified as BPSK reduced from 18.4%to 5.0%,effectively alleviating confusion among similar categories.关键词
水声通信信号/调制识别/双分支神经网络/预训练音频编码器/决策融合Key words
underwater acoustic communication signal/modulation recognition/dual-branch neural network/pre-trained audio encoder/decision fusion分类
信息技术与安全科学引用本文复制引用
黄子浩,满晓云,王劭婷,王晓燕,安良..基于双分支神经网络的水声通信调制识别方法[J].数字海洋与水下攻防,2026,9(3):257-267,11.基金项目
中央高校基本科研业务费专项资金资助(2242025F20003). （2242025F20003）
