无线电工程2024，Vol.54Issue(10)：2355-2361,7.DOI:10.3969/j.issn.1003-3106.2024.10.010
基于对抗残差网络的复杂海况舰船目标类型识别技术研究
Research on Ship Target Type Recognition Technology Under Complex Sea Conditions Based on Adversarial Residual Neural Network
摘要
Abstract
In recent years,with the application of deep learning theory in the field of underwater acoustics,significant progress has been made in the research of underwater target recognition technology.However,in engineering application practice,the recognition model obtained by combining traditional feature extraction and classifier methods is difficult to maintain laboratory performance.The complex and variable ocean channels cause severe distortion of the acoustic signal before sensor reception,leading to feature mismatch and over-fitting problems in the recognition algorithm,and the algorithm performance drops sharply.The Adversarial Residual Neural Network(ARNN)model is proposed to solve the above issue.The model uses a Gradient Reversal Layer(GRL)structure and dual label adversarial training framework to compensate for the differences in channel propagation under different hydrological conditions,enabling the algorithm to focus on features that can characterize the essence of the target,with stronger robustness and higher recognition rate.To verify its effectiveness,two experiments are designed,using the mechanical radiation noise signals of ship targets collected multiple times in different sea areas and hydrological conditions in the South China Sea.Training and testing sample sets are created to train and test the algorithm model.The results show that compared to traditional network models such as Support Vector Machine(SVM),Convolutional Neural Network(CNN)and Residual Network(ResNet)model,the proposed ARNN model can effectively alleviate feature mismatch and over-fitting problems,making the model portable under different hydrological conditions and solving key problems in the application of artificial intelligence technology in water target recognition engineering.关键词
水中目标识别/特征失配/过拟合/对抗训练/梯度反向层Key words
underwater target recognition/feature mismatch/over-fitting/adversarial training/GRL分类
信息技术与安全科学引用本文复制引用
张博轩,王少博,赵天白,罗恒光,王大宇..基于对抗残差网络的复杂海况舰船目标类型识别技术研究[J].无线电工程,2024,54(10):2355-2361,7.基金项目
国家自然科学基金(U20B2071)National Natural Science Foundation of China(U20B2071) （U20B2071）
