An Overview on Underwater Acoustic Passive Target Recognition Based on Deep Learning

ZHANG Qi; DA Lianglong; WANG Chao; ZHANG Yanhou; ZHUO Jianghao

doi:10.11999/JEIT221301

Volume 45 Issue 11

Nov. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(11): 4190-4202

ZHANG Qi, DA Lianglong, WANG Chao, ZHANG Yanhou, ZHUO Jianghao. An Overview on Underwater Acoustic Passive Target Recognition Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2023, 45(11): 4190-4202. doi: 10.11999/JEIT221301

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ZHANG Qi, DA Lianglong, WANG Chao, ZHANG Yanhou, ZHUO Jianghao. An Overview on Underwater Acoustic Passive Target Recognition Based on Deep Learning[J]. Journal of Electronics & Information Technology, 2023, 45(11): 4190-4202. doi: 10.11999/JEIT221301

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An Overview on Underwater Acoustic Passive Target Recognition Based on Deep Learning

doi: 10.11999/JEIT221301

ZHANG Qi^{1, 2, 3},
DA Lianglong^{1, 2
,
,},
WANG Chao^{1, 2},
ZHANG Yanhou^{1, 2},
ZHUO Jianghao^{1, 2}

1.
Naval Submarine Academy, Qingdao 266199, China
2.
Laoshan Laboratory, Qingdao 266237, China
3.
Qingdao Institute of Collaborative Innovation, Qingdao 266071, China

Funds: The National Key R&D Program of China (2021YFC3100900), The Financially Supported by Laoshan Laboratory (LSKJ202201100), The Innovation Plan of Institute of Collaborative Innovation (LYY-2022-05)

Received Date: 2022-10-14
Rev Recd Date: 2023-03-31

Available Online: 2023-04-06

Publish Date: 2023-11-28

Abstract

Abstract

Passive sonar detects targets by receiving radiated noise signals emitted from the targets. Underwater acoustic target recognition is an important research area in the underwater acoustic engineering field to identify individual targets by analyzing underwater acoustic signals. As a research hotspot in various fields in recent years, deep learning has attracted considerable attention from scholars for its application to the underwater acoustic target recognition field. Based on the step framework of underwater acoustic target recognition, two typical deep network models are introduced. Herein, two major implications of deep learning in the underwater acoustic target recognition field are summarized. The key issues and research progress in recent years are investigated for deep learning as a classifier based on features such as spectrograms and mel-frequency cepstrum coefficient and for deep learning as a signal processing tool based on signal processing methods such as data enhancement and data denoising. The development trend of this field is explored from three aspects, namely, data-driven, feature-driven, and model-driven, to promote the development of underwater acoustic target recognition.
- Underwater acoustic target recognition,
- Deep learning,
- Signal processing,
- Feature extraction

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References(67)

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