Range estimation of few-shot underwater sound source in shallow water based on transfer learning and residual CNN

doi:10.23919/JSEE.2023.000095

Journal of Systems Engineering and Electronics ›› 2023, Vol. 34 ›› Issue (4): 839-850.doi: 10.23919/JSEE.2023.000095

• • 上一篇

收稿日期:2022-07-05 接受日期:2023-06-06 出版日期:2023-08-18 发布日期:2023-08-28

Range estimation of few-shot underwater sound source in shallow water based on transfer learning and residual CNN

Qihai YAO¹^,²(), Yong WANG¹^,²^,*(), Yixin YANG¹^,²()

¹ School of Marine Science and Technology, Northwestern Polytechnical University, Xi’an 710072, China
² Shaanxi Key Laboratory of Underwater Information Technology, Xi’an 710072, China

Received:2022-07-05 Accepted:2023-06-06 Online:2023-08-18 Published:2023-08-28
Contact: Yong WANG E-mail:2019260659@mail.nwpu.edu.cn;yongwang@nwpu.edu.cn;yxyang@nwpu.edu.cn
About author:
YAO Qihai was born in 1997. He received his Master ’s degree in ship and ocean engineering from Northwestern Polytechnical University in 2022. He is pursuing his Ph.D. degree at Northwestern Polytechnical University. In 2020, he completed an exchange project of the Oxford prospects program of the University of Oxford, UK, and obtained an excellent graduation certificate. His main research direction is the application of machine learning in array signal processing. E-mail: 2019260659@mail.nwpu.edu.cn

WANG Yong was born in 1987. He received his B.S. degree in applied electronic engineering and M.S. and Ph.D. degrees in underwater acoustic engineering from Northwestern Polytechnical University (NPU), Xi ’an, China, in 2009, 2011, and 2015, respectively. From 2016 to 2017, he was a post-doctoral fellow with Xi ’an Jiaotong University, Xi’an, China. He is currently an associate professor with the School of Marine Science and Technology, NPU. His research interests include array signal processing, parameter estimation, and sonar signal processing. E-mail: yongwang@nwpu.edu.cn

YANG Yixin was born in 1975. He received his B.S. degree in applied electronic engineering and M.S. and Ph.D. degrees in underwater acoustic engineering from Northwestern Polytechnical University (NPU), Xi ’an, China, in 1997, 1999, and 2002, respectively. From June 2002 to June 2004, he was a research fellow with the School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. Since July 2004, he has been with the School of Marine Science and Technology, NPU, where he became a professor in 2006. He is currently the Vice President of NPU, the Chair of the Underwater Acoustics Committee of the Acoustical Society of China, and a member of the Acoustical Society of America. His research interests include acoustic array signal processing, spectral estimation, and their applications. E-mail: yxyang@nwpu.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (11974286;11904274) and the Shaanxi Young Science and Technology Star Program (2021KJXX-07), and the fundamental research funding for characteristic disciplines (G2022WD0235)

摘要/Abstract

Abstract:

Taking the real part and the imaginary part of complex sound pressure of the sound field as features, a transfer learning model is constructed. Based on the pre-training of a large amount of underwater acoustic data in the preselected sea area using the convolutional neural network (CNN), the few-shot underwater acoustic data in the test sea area are retrained to study the underwater sound source ranging problem. The S5 voyage data of SWellEX-96 experiment is used to verify the proposed method, realize the range estimation for the shallow source in the experiment, and compare the range estimation performance of the underwater target sound source of four methods: matched field processing (MFP), generalized regression neural network (GRNN), traditional CNN, and transfer learning. Experimental data processing results show that the transfer learning model based on residual CNN can effectively realize range estimation in few-shot scenes, and the estimation performance is remarkably better than that of other methods.

Key words: transfer learning, residual convolutional neural network (CNN), few shot, vertical array, range estimation

. [J]. Journal of Systems Engineering and Electronics, 2023, 34(4): 839-850.

Qihai YAO, Yong WANG, Yixin YANG. Range estimation of few-shot underwater sound source in shallow water based on transfer learning and residual CNN[J]. Journal of Systems Engineering and Electronics, 2023, 34(4): 839-850.

图/表 14

参考文献 31

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Method	MFP	GRNN			CNN			Transfer learning
Method	MFP	T1	T2	T1/T2	T1	T2	T1/T2	Transfer learning
Narrowband	55.6925	19.0144	12.9857	10.3732	17.5382	7.5285	5.8026	4.0886
Broadband	20.2007	17.4170	10.3911	8.1141	16.7749	6.9176	5.0352	2.9136

Method	GRNN			CNN			Transfer learning
Method	T1	T2	T1/T2	T1	T2	T1/T2	Transfer learning
Training time/s	20.5	9.7	29.2	50.8	32.9	89.3	95.2
Testing time/s	2.3	2.1	2.4	2.8	2.5	3.1	2.9

Range estimation of few-shot underwater sound source in shallow water based on transfer learning and residual CNN

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