基于深度学习的水声被动目标识别研究综述

张奇; 笪良龙; 王超; 张延厚; 禚江浩

doi:10.11999/JEIT221301

基于深度学习的水声被动目标识别研究综述

doi: 10.11999/JEIT221301

张奇^{1, 2, 3},
笪良龙^{1, 2, ,},
王超^{1, 2},
张延厚^{1, 2},
禚江浩^{1, 2}

1.
海军潜艇学院青岛 266199
2.
崂山实验室青岛 266237
3.
青岛协同创新研究院青岛 266071

基金项目: 国家重点研发计划(2021YFC3100900)，崂山实验室科技创新项目(LSKJ202201100)，青岛协同创新研究院创新计划(LYY-2022-05)

详细信息

作者简介:
张奇：男，博士生，研究方向为水声目标识别、深度学习

笪良龙：男，博士，教授，研究方向为海洋环境效应

王超：男，博士，副研究员，研究方向为水声信号处理

张延厚：男，博士生，研究方向为信息融合

禚江浩：男，博士，助理研究员，研究方向为水声信号处理

通讯作者:
笪良龙　 liangld@126.com

中图分类号: TN911.7; O427.9
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- 被引次数: 0
出版历程
- 收稿日期: 2022-10-14
- 修回日期: 2023-03-31
- 网络出版日期: 2023-04-06
- 刊出日期: 2023-11-28

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

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)

摘要

摘要: 被动声呐通过接收目标自身发出的辐射噪声信号进行目标探测。水声目标识别通过分析水声信号来判别目标个体，是水声工程领域的重点研究方向。深度学习作为近年来各领域的研究热点，其在水声目标识别领域中的应用引起了学者的广泛关注。该文以水声目标识别的步骤框架为切入，介绍了典型深度网络模型；总结出了深度学习在水声目标识别领域中的两大内涵：围绕时频谱、梅尔倒谱系数等特征调研了近几年深度学习作为分类器的关键问题以及研究进展，围绕数据增强、数据降噪等信号处理手段调研了近几年深度学习作为信号处理工具的关键问题以及研究进展；并从数据驱动、特征驱动、模型驱动3个方面对该领域的发展趋势进行展望，旨在推动水声目标识别领域的发展。
- 水声目标识别 /
- 深度学习 /
- 信号处理 /
- 特征提取
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

HTML全文

图 1 水声目标识别流程框架

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图 2 本文框架

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图 3 CNN结构示意图

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图 4 LSTM内部结构

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图 5 DEMON谱提取流程

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图 6 中国东海海域某实测水声样本

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参考文献(67)

[1]	乌立克 R J, 洪申, 译. 工程水声原理[M]. 北京: 国防工业出版社, 1972: 1–2. URICK R J, HONG Shen. translation. Principles of Engineering Underwater Sound[M]. Beijing: National Defense Industry Press, 1972: 1–2.
[2]	HINTON G E and SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504–507. doi: 10.1126/science.1127647
[3]	KRIZHEVSKY A, SUTSKEVER I, and HINTON G E. Imagenet classification with deep convolutional neural networks[C]. Proceedings of the 25th International Conference on Neural Information Processing Systems, Lake Tahoe, USA, 2012.
[4]	LIU Ze, LIN Yutong, CAO Yue, et al. Swin transformer: Hierarchical vision transformer using shifted windows[C]. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, Canada, 2021: 9992–10002.
[5]	VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]. Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, USA 2017.
[6]	GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]. Proceedings of the 27th International Conference on Neural Information Processing Systems, Montreal, Canada, 2014.
[7]	PARK S R and LEE J. A fully convolutional neural network for speech enhancement[C]. Interspeech 2017, 18th Annual Conference of the International Speech Communication Association, Stockholm, Sweden, 2017.
[8]	HU Gang, WANG Kejun, and LIU Liangliang. Underwater acoustic target recognition based on depthwise separable convolution neural networks[J]. Sensors, 2021, 21(4): 1429. doi: 10.3390/s21041429
[9]	李理, 李向欣, 殷敬伟. 基于生成对抗网络的舰船辐射噪声分类方法研究[J]. 电子与信息学报, 2022, 44(6): 1974–1983. doi: 10.11999/JEIT211077 LI Li, LI Xiangxin, and YIN Jingwei. Research on classification algorithm of ship radiated noise data based on generative adversarial network[J]. Journal of Electronics&Information Technology, 2022, 44(6): 1974–1983. doi: 10.11999/JEIT211077
[10]	卢安安. 基于深度学习方法的水下声音目标识别研究[D]. [硕士论文], 哈尔滨工程大学, 2017. LU Anan. Underwater acoustic classification based on deep learning[D]. [Master dissertation], Harbin Engineering University, 2017.
[11]	郝宇昕. 基于深度神经网络的舰船辐射噪声特征分类技术[D]. [硕士论文], 哈尔滨工程大学, 2019. HAO Yuxin. Ship radiated noise classification method based on deep neural network[D]. [Master dissertation], Harbin Engineering University, 2019.
[12]	姜岩松. 基于生成对抗网络的水声信号识别与分离研究[D]. [硕士论文], 哈尔滨工程大学, 2021. JIANG Yansong. Research on underwater acoustic signal recognition and separation based on generative adversarial network[D]. [Master dissertation], Harbin Engineering University, 2021.
[13]	胡钢. 基于深度学习的水下目标识别和运动行为分析技术研究[D]. [博士论文], 哈尔滨工程大学, 2021. HU Gang. Research on underwater target recognition and motion behavior analysis technology based on deep learning[D]. [Ph. D. dissertation], Harbin Engineering University, 2021.
[14]	LI Junhao and YANG Honghui. The underwater acoustic target timbre perception and recognition based on the auditory inspired deep convolutional neural network[J]. Applied Acoustics, 2021, 182: 108210. doi: 10.1016/j.apacoust.2021.108210
[15]	YANG Honghui, ZHENG Kaifeng, and LI Junhao. Open set recognition of underwater acoustic targets based on GRU-CAE collaborative deep learning network[J]. Applied Acoustics, 2022, 193: 108774. doi: 10.1016/j.apacoust.2022.108774
[16]	黄擎, 曾向阳. 小波分解和改进卷积神经网络相融合的水声目标识别方法[J]. 哈尔滨工程大学学报, 2022, 43(2): 159–165. doi: 10.11990/jheu.202011040 HUANG Qing and ZENG Xiangyang. An underwater acoustic target recognition method combining wavelet decomposition and an improved convolutional neural network[J]. Journal of Harbin Engineering University, 2022, 43(2): 159–165. doi: 10.11990/jheu.202011040
[17]	薛灵芝, 曾向阳, 杨爽. 基于生成对抗网络的水声目标识别算法[J]. 兵工学报, 2021, 42(11): 2444–2452. doi: 10.3969/j.issn.1000-1093.2021.11.018 XUE Lingzhi, ZENG Xiangyang, and YANG Shuang. Underwater acoustic target recognition algorithm based on generative adversarial networks[J]. Acta Armamentarii, 2021, 42(11): 2444–2452. doi: 10.3969/j.issn.1000-1093.2021.11.018
[18]	杨爽, 曾向阳. 基于多尺度稀疏简单循环单元模型的水声目标识别方法[J]. 哈尔滨工程大学学报, 2022, 43(7): 958–964. doi: 10.11990/jheu.202105048 YANG Shuang and ZENG Xiangyang. Underwater acoustic target recognition method based on the multi-scale sparse simple recurrent unit model[J]. Journal of Harbin Engineering University, 2022, 43(7): 958–964. doi: 10.11990/jheu.202105048
[19]	ZHOU Xingyue, YANG Kunde, and DUAN Rui. Deep learning based on striation images for underwater and surface target classification[J]. IEEE Signal Processing Letters, 2019, 26(9): 1378–1382. doi: 10.1109/LSP.2019.2919102
[20]	IRFAN M, ZHENG Jiangbin, ALI S, et al. DeepShip: An underwater acoustic benchmark dataset and a separable convolution based autoencoder for classification[J]. Expert Systems with Applications, 2021, 183: 115270. doi: 10.1016/j.eswa.2021.115270
[21]	IRFAN M, ZHENG Jiangbin, IQBAL M, et al. Brain inspired lifelong learning model based on neural based learning classifier system for underwater data classification[J]. Expert Systems with Applications, 2021, 186: 115798. doi: 10.1016/j.eswa.2021.115798
[22]	ZHANG Qi, DA Lianglong, ZHANG Yanhou, et al. Integrated neural networks based on feature fusion for underwater target recognition[J]. Applied Acoustics, 2021, 182: 108261. doi: 10.1016/j.apacoust.2021.108261
[23]	张少康, 王超, 田德艳, 等. 长短时记忆网络水下目标噪声智能识别方法[J]. 舰船科学技术, 2019, 41(12): 181–185. doi: 10.3404/j.issn.1672-7649.2019.12.035 ZHANG Shaokang, WANG Chao, TIAN Deyan, et al. Intelligent recognition of underwater target noise based on long short-term memory networks[J]. Ship Science and Technology, 2019, 41(12): 181–185. doi: 10.3404/j.issn.1672-7649.2019.12.035
[24]	张少康, 王超, 孙芹东. 基于多类别特征融合的水声目标噪声识别分类技术[J]. 西北工业大学学报, 2020, 38(2): 366–376. doi: 10.1051/jnwpu/20203820366 ZHANG Shaokang, WANG Chao, and SUN Qindong. Underwater target noise recognition and classification technology based on multi-classes feature fusion[J]. Journal of Northwestern Polytechnical University, 2020, 38(2): 366–376. doi: 10.1051/jnwpu/20203820366
[25]	程玉胜, 邱家兴, 刘振, 等. 水声被动目标识别技术挑战与展望[J]. 应用声学, 2019, 38(4): 653–659. doi: 10.11684/j.issn.1000-310X.2019.04.023 CHENG Yusheng, QIU Jiaxing, LIU Zhen, et al. Challenges and prospects of underwater acoustic passive target recognition technology[J]. Journal of Applied Acoustics, 2019, 38(4): 653–659. doi: 10.11684/j.issn.1000-310X.2019.04.023
[26]	徐源超, 蔡志明, 孔晓鹏. 基于双对数谱和卷积网络的船舶辐射噪声分类[J]. 电子与信息学报, 2022, 44(6): 1947–1955. doi: 10.11999/JEIT211407 XU Yuanchao, CAI Zhiming, and KONG Xiaopeng. Classification of ship radiated noise based on bi-logarithmic scale spectrum and convolutional network[J]. Journal of Electronics&Information Technology, 2022, 44(6): 1947–1955. doi: 10.11999/JEIT211407
[27]	徐源超, 蔡志明. 水声目标分类算法性能评估[J]. 哈尔滨工程大学学报, 2020, 41(10): 1559–1565. doi: 10.11990/jheu.202007114 XU Yuanchao and CAI Zhiming. Performance evaluation on the algorithm of underwater acoustic target classification[J]. Journal of Harbin Engineering University, 2020, 41(10): 1559–1565. doi: 10.11990/jheu.202007114
[28]	徐萍. 水声目标辐射噪声特征提取与识别技术研究[D]. [硕士论文], 东南大学, 2019. XU Ping. Research on feature extraction and recognition technology of underwater acoustic target radiated noise[D]. [Master dissertation], Southeast University, 2019.
[29]	张昊. 水声目标辐射噪声信号增强与特征辨识技术研究[D]. [硕士论文], 东南大学, 2021. ZHANG Hao. Research on enhancement and feature recognition technology of radiated noise signal of underwater acoustic target[D]. [Master dissertation], Southeast University, 2021.
[30]	倪俊帅, 赵梅, 胡长青. 基于深度学习的舰船辐射噪声多特征融合分类[J]. 声学技术, 2020, 39(3): 366–371. doi: 10.16300/j.cnki.1000-3630.2020.03.019 NI Junshuai, ZHAO Mei, and HU Changqing. Multi-feature fusion classification of ship radiated noise based on deep learning[J]. Technical Acoustics, 2020, 39(3): 366–371. doi: 10.16300/j.cnki.1000-3630.2020.03.019
[31]	李琛, 黄兆琼, 徐及, 等. 使用深度学习的多通道水下目标识别[J]. 声学学报, 2020, 45(4): 506–514. doi: 10.15949/j.cnki.0371-0025.2020.04.007 LI Chen, HUANG Zhaoqiong, XU Ji, et al. Multi-channel underwater target recognition using deep learning[J]. Acta Acustica, 2020, 45(4): 506–514. doi: 10.15949/j.cnki.0371-0025.2020.04.007
[32]	FILHO W S, DE SEIXAS J M, and DE MOURA N N. Preprocessing passive sonar signals for neural classification[J]. IET Radar,Sonar&Navigation, 2011, 5(6): 605–612. doi: 10.1049/iet-rsn.2010.0157
[33]	DE B A, BARROS R E, and EBECKEN N F F. Development of a ship classification method based on Convolutional neural network and Cyclostationarity Analysis[J]. Mechanical Systems and Signal Processing, 2022, 170: 108778. doi: 10.1016/j.ymssp.2021.108778
[34]	MOSAVI M R, KHISHE M, NASERI M J, et al. Multi-layer perceptron neural network utilizing adaptive best-mass gravitational search algorithm to classify sonar dataset[J]. Archives of Acoustics, 2019, 44(1): 137–151. doi: 10.24425/aoa.2019.126360
[35]	JAHROMI M S, BAGHERI V, ROSTAMI H, et al. Feature extraction in fractional fourier domain for classification of passive sonar signals[J]. Journal of Signal Processing Systems, 2019, 91(5): 511–520. doi: 10.1007/s11265-018-1347-x
[36]	KHISHE M and MOSAVI M R. Classification of underwater acoustical dataset using neural network trained by Chimp Optimization Algorithm[J]. Applied Acoustics, 2020, 157: 107005. doi: 10.1016/j.apacoust.2019.107005
[37]	SAFFARI A, KHISHE M, and ZAHIRI S H. Fuzzy-ChOA: An improved chimp optimization algorithm for marine mammal classification using artificial neural network[J]. Analog Integrated Circuits and Signal Processing, 2022, 111(3): 403–417. doi: 10.1007/s10470-022-02014-1
[38]	BAQAR M and ZAIDI S S H. Performance evaluation of linear and multi-linear subspace learning techniques for object classification based on underwater acoustics[C]. 2017 14th International Bhurban Conference on Applied Sciences and Technology, Islamabad, Pakistan, 2017: 675–683.
[39]	GONZÁLEZ-HERNÁNDEZ F R, SÁNCHEZ-FERNÁNDEZ L P, SUÁREZ-GUERRA S, et al. Marine mammal sound classification based on a parallel recognition model and octave analysis[J]. Applied Acoustics, 2017, 119: 17–28. doi: 10.1016/j.apacoust.2016.11.016
[40]	ZHONG Ming, TORTEROTOT M, BRANCH T A, et al. Detecting, classifying, and counting blue whale calls with Siamese neural networks[J]. The Journal of the Acoustical Society of America, 2021, 149(5): 3086–3094. doi: 10.1121/10.0004828
[41]	COLE A M. Automated open circuit scuba diver detection with low cost passive sonar and machine learning[D]. [Master dissertation], Massachusetts Institute of Technology, 2019.
[42]	BIANCO M J, GERSTOFT P, TRAER J, et al. Machine learning in acoustics: Theory and applications[J]. The Journal of the Acoustical Society of America, 2019, 146(5): 3590–3628. doi: 10.1121/1.5133944
[43]	周飞燕, 金林鹏, 董军. 卷积神经网络研究综述[J]. 计算机学报, 2017, 40(6): 1229–1251. doi: 10.11897/SP.J.1016.2017.01229 ZHOU Feiyan, JIN Linpeng, and DONG Jun. Review of convolutional neural network[J]. Chinese Journal of Computers, 2017, 40(6): 1229–1251. doi: 10.11897/SP.J.1016.2017.01229
[44]	GRAVES A. Long short-term memory[M]. GRAVES A. Supervised Sequence Labelling with Recurrent Neural Networks. Berlin: Springer, 2012: 37–45.
[45]	GOODFELLOW I, BENGIO Y, and COURVILLE A. Deep Learning[M]. Cambridge: MIT Press, 2016: 243.
[46]	张健. 基于深度学习的水下目标识别的研究[D]. [硕士论文], 电子科技大学, 2020. ZHANG Jian. Research on underwater target recognition based on deep learning[D]. [Master dissertation], University of Electronic Science and Technology of China, 2020.
[47]	任晨曦, 王黎明, 韩星程, 等. 基于联合神经网络的水声目标识别方法[J]. 舰船科学技术, 2022, 44(1): 136–141. doi: 10.3404/j.issn.1672-7649.2022.01.026 REN Chenxi, WANG Liming, HAN Xingcheng, et al. Underwater acoustic target recognition method based on joint neural network[J]. Ship Science and Technology, 2022, 44(1): 136–141. doi: 10.3404/j.issn.1672-7649.2022.01.026
[48]	HAN Xingcheng, REN Chenxi, WANG Liming, et al. Underwater acoustic target recognition method based on a joint neural network[J]. PloS One, 2022, 17(4): e0266425. doi: 10.1371/journal.pone.0266425
[49]	曾赛, 杜选民. 水下目标多模态深度学习分类识别研究[J]. 应用声学, 2019, 38(4): 589–595. doi: 10.11684/j.issn.1000-310X.2019.04.016 ZENG Sai and DU Xuanmin. Multimodal underwater target recognition method based on deep learning[J]. Journal of Applied Acoustics, 2019, 38(4): 589–595. doi: 10.11684/j.issn.1000-310X.2019.04.016
[50]	杨路飞, 章新华, 吴秉坤, 等. 基于MFCC特征的被动水声目标深度学习分类方法[J]. 舰船科学技术, 2020, 42(10): 129–133. doi: 10.3404/j.issn.1672-7649.2020.10.025 YANG Lufei, ZHANG Xinhua, WU Bingkun, et al. Research on the classification method of passive acoustic target depth learning based on MFCC[J]. Ship Science and Technology, 2020, 42(10): 129–133. doi: 10.3404/j.issn.1672-7649.2020.10.025
[51]	AL-BETAR M A, ALYASSERI Z A A, AWADALLAH M A, et al. Coronavirus herd immunity optimizer (CHIO)[J]. Neural Computing and Applications, 2021, 33(10): 5011–5042. doi: 10.21203/rs.3.rs-27214/v1
[52]	史广智, 胡均川. 舰船噪声调制谱谐波族结构特性理论分析[J]. 声学学报, 2007, 32(1): 19–25. doi: 10.3321/j.issn:0371-0025.2007.01.004 SHI Guangzhi and HU Junchuan. Theoretical analysis of the structure law of ship radiated-noise demodulation spectrum harmonic clan feature[J]. Acta Acustica, 2007, 32(1): 19–25. doi: 10.3321/j.issn:0371-0025.2007.01.004
[53]	程玉胜, 张宝华, 高鑫, 等. 船舶辐射噪声解调谱相位耦合特性与应用[J]. 声学学报, 2012, 37(1): 25–29. doi: 10.15949/j.cnki.0371-0025.2012.01.004 CHENG Yusheng, ZHANG Baohua, GAO Xin, et al. Phase-coupling characteristics of ship radiated-noise demodulation spectrum and application[J]. Acta Acustica, 2012, 37(1): 25–29. doi: 10.15949/j.cnki.0371-0025.2012.01.004
[54]	张奇, 张延厚, 贾书阳, 等. 一种脉冲分布噪声下DEMON谱提取方法[C]. 中国声学学会水声学分会2021-2022年学术会议论文集, 青岛, 2021: 261–263. ZHANG Qi, ZHANG Yanhou, JIA Shuyang, et al. A DEMON spectrum extraction method under pulse distribution noise[C]. Hydroacoustics Branch of Chinese Acoustical Society, Qingdao, China, 2021: 261–263.
[55]	刘振, 邱家兴, 程玉胜. 深度神经网络在螺旋桨叶片数识别中的应用[J]. 声学技术, 2019, 38(4): 459–463. doi: 10.16300/j.cnki.1000-3630.2019.04.017 LIU Zhen, QIU Jiaxing, and CHENG Yusheng. Application of deep neural network in blade-number recognition of ship propeller[J]. Technical Acoustics, 2019, 38(4): 459–463. doi: 10.16300/j.cnki.1000-3630.2019.04.017
[56]	LU Jiamin, SONG Sanming, HU Zhiqiang, et al. Fundamental frequency detection of underwater acoustic target using DEMON spectrum and CNN network[C]. 2020 3rd International Conference on Unmanned Systems, Harbin, China, 2020: 778–784.
[57]	GONZALEZ S and BROOKES M. A pitch estimation filter robust to high levels of noise (PEFAC)[C]. 19th European Signal Processing Conference, Barcelona, Spain, 2011: 451–455.
[58]	卢佳敏, 宋三明, 景严, 等. 基于DEMON谱和LSTM网络的水下运动目标噪声基频检测[J]. 应用声学, 2021, 40(5): 745–753. doi: 10.11684/j.issn.1000-310X.2021.05.013 LU Jiamin, SONG Sanming, JING Yan, et al. Fundamental frequency detection of underwater target noises using DEMON spectrum and LSTM network[J].Journal of Applied Acoustics, 2021, 40(5): 745–753. doi: 10.11684/j.issn.1000-310X.2021.05.013
[59]	白敬贤, 高天德, 夏润鹏. 基于DEMON谱信息提取算法的目标识别方法研究[J]. 声学技术, 2017, 36(1): 88–92. doi: 10.16300/j.cnki.1000-3630.2017.01.016 BAI Jingxian, GAO Tiande, and XIA Runpeng. Target recognition based on the information extraction algorithm of DEMON spectrum[J]. Technical Acoustics, 2017, 36(1): 88–92. doi: 10.16300/j.cnki.1000-3630.2017.01.016
[60]	杨日杰, 郑晓庆, 韩建辉, 等. 基于序列匹配的螺旋桨轴频自动提取方法[J]. 振动与冲击, 2018, 37(16): 57–61. doi: 10.13465/j.cnki.jvs.2018.16.009 YANG Rijie, ZHENG Xiaoqing, HAN Jianhui, et al. An automatic extraction method of propeller shaft frequency based on sequence matching[J]. Journal of Vibration and Shock, 2018, 37(16): 57–61. doi: 10.13465/j.cnki.jvs.2018.16.009
[61]	王小宇, 李凡, 曹琳, 等. 改进的卷积神经网络实现端到端的水下目标自动识别[J]. 信号处理, 2020, 36(6): 958–965. doi: 10.16798/j.issn.1003-0530.2020.06.018 WANG Xiaoyu, LI Fan, CAO Lin, et al. End to end underwater targets recognition using the modified convolutional neural network[J]. Journal of Signal Processing, 2020, 36(6): 958–965. doi: 10.16798/j.issn.1003-0530.2020.06.018
[62]	LIN Min, CHEN Qiang, and YAN Shuicheng. Network in network[EB/OL]. https://arxiv.org/abs/1312.4400#, 2013.
[63]	IOFFE S and SZEGEDY C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[C]. Proceedings of the 32nd International Conference on International Conference on Machine Learning, Lille, France, 2015: 448–456.
[64]	李悦, 马晓川, 王磊, 等. 非高斯环境下的深度学习脉冲信号去噪与重构[J]. 应用声学, 2021, 40(1): 131–141. doi: 10.11684/j.issn.1000-310X.2021.01.016 LI Yue, MA Xiaochuan, WANG Lei, et al. Using deep learning to de-noise and reconstruct pulse signals in non-Gaussian environment[J]. Journal of Applied Acoustics, 2021, 40(1): 131–141. doi: 10.11684/j.issn.1000-310X.2021.01.016
[65]	KE Xiaoquan, YUAN Fei, and CHENG En. Integrated optimization of underwater acoustic ship-radiated noise recognition based on two-dimensional feature fusion[J]. Applied Acoustics, 2020, 159: 107057. doi: 10.1016/j.apacoust.2019.107057
[66]	WU Zonghan, PAN Shirui, CHEN Fengwen, et al. A comprehensive survey on graph neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(1): 4–24. doi: 10.1109/TNNLS.2020.2978386
[67]	FEDUS W, ZOPH B, and SHAZEER N. Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity[J]. The Journal of Machine Learning Research, 2023, 23(1): 120.