Surface and underwater target classification under limited sample sizes based on sound field elevation structure

doi:10.1088/1674-1056/ade389

中国物理B ›› 2025, Vol. 34 ›› Issue (11): 114301-114301.doi: 10.1088/1674-1056/ade389

Surface and underwater target classification under limited sample sizes based on sound field elevation structure

Yixin Miao(苗艺馨)^1,2,3, Jin Fu(付进)^4,†, and Xue Wang(王雪)⁵

1 National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China;
2 Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University), Ministry of Industry and Information Technology, Harbin 150001, China;
3 College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China;
4 School of Electronic Information Engineering, Beihang University, Beijing 100094, China;
5 Hangzhou Applied Acoustics Research Institute, Hangzhou 310023, China

收稿日期:2025-04-21 修回日期:2025-06-08 接受日期:2025-06-11 发布日期:2025-10-30
基金资助:
This work was supported by the National Natural Science Foundation of China (Grant Nos. 62471024 and 62301183) and the Open Research Fund of Hanjiang Laboratory (KF2024001).

Surface and underwater target classification under limited sample sizes based on sound field elevation structure

Yixin Miao(苗艺馨)^1,2,3, Jin Fu(付进)^4,†, and Xue Wang(王雪)⁵

1 National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China;
2 Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University), Ministry of Industry and Information Technology, Harbin 150001, China;
3 College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China;
4 School of Electronic Information Engineering, Beihang University, Beijing 100094, China;
5 Hangzhou Applied Acoustics Research Institute, Hangzhou 310023, China

Received:2025-04-21 Revised:2025-06-08 Accepted:2025-06-11 Published:2025-10-30
Contact: Jin Fu E-mail:fujin@buaa.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (Grant Nos. 62471024 and 62301183) and the Open Research Fund of Hanjiang Laboratory (KF2024001).

摘要/Abstract

摘要： Surface/underwater target classification is a key topic in marine information research. However, the complex underwater environment, coupled with the diversity of target types and their variable characteristics, presents significant challenges for classifier design. For shallow-water waveguides with a negative thermocline, a residual neural network (ResNet) model based on the sound field elevation structure is constructed. This model demonstrates robust classification performance even when facing low signal-to-noise ratios and environmental mismatches. Meanwhile, to address the reduced generalization ability caused by limited labeled acoustic data, an improved ResNet model based on unsupervised domain adaptation (“proposed UDA-ResNet”) is further constructed. This model incorporates data on simulated elevation structures of the sound field to augment the training process. Adversarial training is employed to extract domain-invariant features from simulated and trial data. These strategies help reduce the negative impact caused by domain differences. Experimental results demonstrate that the proposed method shows strong surface/underwater target classification ability under limited sample sizes, thus confirming its feasibility and effectiveness.

关键词: sound field elevation structure, surface/underwater target classification, limited sample size, unsupervised domain adaptation

Abstract: Surface/underwater target classification is a key topic in marine information research. However, the complex underwater environment, coupled with the diversity of target types and their variable characteristics, presents significant challenges for classifier design. For shallow-water waveguides with a negative thermocline, a residual neural network (ResNet) model based on the sound field elevation structure is constructed. This model demonstrates robust classification performance even when facing low signal-to-noise ratios and environmental mismatches. Meanwhile, to address the reduced generalization ability caused by limited labeled acoustic data, an improved ResNet model based on unsupervised domain adaptation (“proposed UDA-ResNet”) is further constructed. This model incorporates data on simulated elevation structures of the sound field to augment the training process. Adversarial training is employed to extract domain-invariant features from simulated and trial data. These strategies help reduce the negative impact caused by domain differences. Experimental results demonstrate that the proposed method shows strong surface/underwater target classification ability under limited sample sizes, thus confirming its feasibility and effectiveness.

Key words: sound field elevation structure, surface/underwater target classification, limited sample size, unsupervised domain adaptation

中图分类号: (Underwater sound)

43.30.-k

43.30.Bp (Normal mode propagation of sound in water) 43.60.Np (Acoustic signal processing techniques for neural nets and learning systems) 07.05.Mh (Neural networks, fuzzy logic, artificial intelligence)

Yixin Miao(苗艺馨), Jin Fu(付进), and Xue Wang(王雪). Surface and underwater target classification under limited sample sizes based on sound field elevation structure[J]. 中国物理B, 2025, 34(11): 114301-114301.

Yixin Miao(苗艺馨), Jin Fu(付进), and Xue Wang(王雪). Surface and underwater target classification under limited sample sizes based on sound field elevation structure[J]. Chin. Phys. B, 2025, 34(11): 114301-114301.

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Surface and underwater target classification under limited sample sizes based on sound field elevation structure

Surface and underwater target classification under limited sample sizes based on sound field elevation structure

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