Underwater Acoustic Signal Detection using Similarity Network Construction and Representation

ZHANG Hongwei; WANG Haiyan; YAN Yongsheng; SHEN Xiaohong

doi:10.11999/JEIT230253

Volume 46 Issue 1

Jan. 2024

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Article Navigation > Journal of Electronics & Information Technology > 2024 > 46(1): 58-66

ZHANG Hongwei, WANG Haiyan, YAN Yongsheng, SHEN Xiaohong. Underwater Acoustic Signal Detection using Similarity Network Construction and Representation[J]. Journal of Electronics & Information Technology, 2024, 46(1): 58-66. doi: 10.11999/JEIT230253

Citation:

ZHANG Hongwei, WANG Haiyan, YAN Yongsheng, SHEN Xiaohong. Underwater Acoustic Signal Detection using Similarity Network Construction and Representation[J]. Journal of Electronics & Information Technology, 2024, 46(1): 58-66. doi: 10.11999/JEIT230253

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Underwater Acoustic Signal Detection using Similarity Network Construction and Representation

doi: 10.11999/JEIT230253

ZHANG Hongwei^{1, 2},
WANG Haiyan^{2, 3
,
,},
YAN Yongsheng^{1, 2},
SHEN Xiaohong^{1, 2}

1.
School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710077, China
2.
Key Laboratory of Ocean Acoustics and Sensing (Northwestern Polytechnical University), Ministry of Industry and Information Technology, Xi’an 710072, China
3.
School of Electronic Information and Artificial Intelligence Shaanxi University of Science and Technology, Xi’an 710021, China

Funds: The National Natural Science Foundation of China (62031021, 62271404, 62201439), The 2023 Innovation Fund for Graduate Students of Northwestern Polytechnical University (CX2023041)

Received Date: 2023-04-11
Rev Recd Date: 2023-07-25

Available Online: 2023-07-26

Publish Date: 2024-01-17

Abstract

Abstract

Underwater acoustic signal detection plays a crucial role in ocean defense systems and has broad applications in civilian domains. However, contemporary underwater acoustic signal detection methods need to be improved for effectiveness when prior information about the target is unavailable. This paper proposes a new algorithm - a similarity network - to address the challenge of underwater target detection in complex oceanic backgrounds. In this method, information geometry and complex network theory are combined, and the problem of measuring node similarity is converted into a geometric problem on a matrix manifold, wherein the similarity between data at different time scales is determined, and a network representation of the time series data is achieved. Concurrently, a graph signal processing theory is introduced to extract the hidden dynamic characteristics of the target signal, thereby achieving underwater acoustic signal detection without prior target information. Further, the effectiveness of this method is demonstrated through research and verification of the simulated and actual. Our results show that the similarity network method is superior to existing network construction and passive target detection methods, can detect underwater acoustic signals more effectively, and can achieve underwater acoustic signal detection without any prior target information.
- Complex network,
- Information geometry,
- Similarity network,
- Acoustic signal detection

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References(27)

References

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