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

被引：0
|
作者：
Zhang Q. [1 ,2 ,3 ]
Da L. [1 ,2 ]
Wang C. [1 ,2 ]
Zhang Y. [1 ,2 ]
Zhuo J. [1 ,2 ]
机构：
[1] Naval Submarine Academy, Qingdao
[2] Laoshan Laboratory, Qingdao
[3] Qingdao Institute of Collaborative Innovation, Qingdao
来源：
Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology | 2023年 / 45卷 / 11期
关键词：
Deep learning; Feature extraction; Signal processing; Underwater acoustic target recognition;
D O I：
10.11999/JEIT221301
中图分类号：
学科分类号：
摘要：
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. © 2023 Science Press. All rights reserved.
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页码：4190 / 4202
页数：12
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