数字海洋与水下攻防2026，Vol.9Issue(2)：118-130,13.DOI:10.19838/j.issn.2096-5753.2026.02.001
水下声学目标识别技术综述:从机器学习到深度学习
Review of Underwater Acoustic Target Recognition Technology:from Machine Learning to Deep Learning
摘要
Abstract
This paper aims to systematically review the development,key techniques,and major challenges of machine learning and deep learning methods for underwater acoustic target recognition,and to clarify current research hotspots and future directions.It first reviews the pipeline of underwater acoustic signal processing,including data preprocessing,feature extraction,and related representation methods,covering physically meaningful features,time-frequency features,auditory perceptual features,and multi-feature fusion strategies.On this basis,traditional machine learning baseline methods based on models such as Support Vector Machines(SVM)and Hidden Markov Models(HMM)are analyzed,and deep learning architectures led by Convolutional Neural Networks(CNNs),Recurrent Neural Networks(RNNs),attention mechanisms,and Transformers are summarized.Meanwhile,the application of transfer learning and data augmentation under few-shot conditions is discussed.Existing studies indicate that underwater acoustic target recognition(UATR)has gradually evolved from shallow statistical learning toward a data-driven paradigm centered on deep neural feature representation,with the combination of time-frequency representations and deep networks becoming a commonly adopted technical route.Multi-feature fusion,transfer learning,and data augmentation can effectively alleviate the problems of low signal-to-noise ratio,limited samples,and class imbalance.Meanwhile,model performance is highly sensitive to dataset partition protocols,and recording-level evaluation provides a more realistic measure of generalization.In addition,accuracy alone is insufficient for evaluating imbalanced multi-class UATR tasks.Future research should focus on standardized benchmarks,more comprehensive evaluation metrics,cross-domain generalization,model interpretability,and lightweight deployment for edge platforms,while promoting a deeper integration of physical priors with deep learning models.关键词
水下声学目标识别/深度学习/机器学习/特征提取/数据增强/迁移学习Key words
underwater acoustic target recognition(UATR)/deep learning/machine learning/feature extraction/data augmentation/transfer learning分类
通用工业技术引用本文复制引用
闫晨红,高若滨,潘光,于洋,鄢社锋,许金鹏,杨炎坤,姚添译,李俊涛..水下声学目标识别技术综述:从机器学习到深度学习[J].数字海洋与水下攻防,2026,9(2):118-130,13.基金项目
国家重点研发计划"集群组网总体设计"(2021YFC2803001,2021YFC2803000). （2021YFC2803001,2021YFC2803000）
