基于被动声呐音频信号的水中目标识别综述

徐齐胜; 许可乐; 窦勇; 高彩丽; 乔鹏; 冯大为; 朱博青

doi:10.16383/j.aas.c230153

基于被动声呐音频信号的水中目标识别综述

doi: 10.16383/j.aas.c230153

徐齐胜^{1, 2,},
许可乐^{1, 2,},
窦勇^{1, 2,},
高彩丽^{1, 2,},
乔鹏^{1, 2,},
冯大为^{1, 2,},
朱博青^{1, 2,}

1.
国防科技大学计算机学院长沙 410073
2.
并行与分布处理国防科技重点实验室长沙 410073

详细信息

作者简介:
徐齐胜：国防科技大学计算机学院硕士研究生. 2021年获得武汉大学学士学位. 主要研究方向为音频信号处理, 并行计算. E-mail: qishengxu@nudt.edu.cn

许可乐：国防科技大学计算机学院副教授. 2017年获得法国巴黎六大博士学位. 主要研究方向为音频信号处理, 机器学习和智能软件系统. 本文通信作者. E-mail: xukelele@163.com

窦勇：国防科技大学并行与分布处理国防科技重点实验室教授. 1995年获得国防科技大学博士学位. 主要研究方向为高性能计算, 智能计算, 机器学习和深度学习. E-mail: yongdou@nudt.edu.cn

高彩丽：国防科技大学计算机学院硕士研究生. 2021年获得南昌大学学士学位. 主要研究方向为人脸伪造检测, 并行优化. E-mail: gaocl@nudt.edu.cn

乔鹏：国防科技大学并行与分布处理国防科技重点实验室助理研究员. 2018年获得国防科技大学博士学位. 主要研究方向为高性能计算, 图像恢复和深度强化学习. E-mail: pengqiao@nudt.edu.cn

冯大为：国防科技大学计算机学院副教授. 2014年获得法国巴黎第十一大学博士学位. 主要研究方向为分布计算与智能软件系统. E-mail: dafeng@nudt.edu.cn

朱博青：国防科技大学博士研究生. 2019年获得国防科技大学硕士学位. 主要研究方向为多模态机器学习, 持续学习和计算声学. E-mail: zhuboq@gmail.com

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出版历程
- 收稿日期: 2023-03-22
- 录用日期: 2023-07-10
- 网络出版日期: 2024-03-14
- 刊出日期: 2024-04-26

A Review of Underwater Target Recognition Based on Passive Sonar Acoustic Signals

XU Qi-Sheng^{1, 2
,},
XU Ke-Le^{1, 2
,},
DOU Yong^{1, 2
,},
GAO Cai-Li^{1, 2
,},
QIAO Peng^{1, 2
,},
FENG Da-Wei^{1, 2
,},
ZHU Bo-Qing^{1, 2
,}

1.
School of Computer Science, National University of Defense Technology, Changsha 410073
2.
National Key Laboratory of Parallel and Distributed Processing, Changsha 410073

More Information

Author Bio:
XU Qi-Sheng　Master student at the School of Computer Science, National University of Defense Technology. He received his bachelor degree from Wuhan University in 2021. His research interest covers audio signal processing and parallel computing

XU Ke-Le　Associate professor at the School of Computer Science, National University of Defense Technology. He received his Ph.D. degree from Paris VI University in 2017. His research interest covers audio signal processing, machine learning, and intelligent software systems. Corresponding author of this paper

DOU Yong　Professor at the National Key Laboratory of Parallel and Distributed Processing, National University of Defense Technology. He received his Ph.D. degree from National University of Defense Technology in 1995. His research interest covers high performance computing, intelligence computing, machine learning, and deep learning

GAO Cai-Li　Master student at the School of Computer Science, National University of Defense Technology. He received his bachelor degree from Nanchang University in 2021. His research interest covers face forgery detection and parallel optimization

QIAO Peng　Assistant researcher at the National Key Laboratory of Parallel and Distributed Processing, National University of Defense Technology. He received his Ph.D. degree from National University of Defense Technology in 2018. His research interest covers high performance computing, image restoration, and deep reinforcement learning

FENG Da-Wei　Associate professor at the School of Computer Science, National University of Defense Technology. He received his Ph.D. degree from Paris-Sud University in 2014. His research interest covers distributed computing and intelligent software systems

ZHU Bo-Qing　Ph.D. candidate at the School of Computer Science, National University of Defense Technology. He received his master degree from National University of Defense Technology in 2019. His research interest covers multi-modal machine learning, continual learning, and computational acoustics

摘要

摘要: 基于被动声呐音频信号的水中目标识别是当前水下无人探测领域的重要技术难题, 在军事和民用领域都应用广泛. 本文从数据处理和识别方法两个层面系统阐述基于被动声呐信号进行水中目标识别的方法和流程. 在数据处理方面, 从基于被动声呐信号的水中目标识别基本流程、被动声呐音频信号分析的数理基础及其特征提取三个方面概述被动声呐信号处理的基本原理. 在识别方法层面, 全面分析基于机器学习算法的水中目标识别方法, 并聚焦以深度学习算法为核心的水中目标识别研究. 本文从有监督学习、无监督学习、自监督学习等多种学习范式对当前研究进展进行系统性的总结分析, 并从算法的标签数据需求、鲁棒性、可扩展性与适应性等多个维度分析这些方法的优缺点. 同时, 还总结该领域中较为广泛使用的公开数据集, 并分析公开数据集应具备的基本要素. 最后, 通过对水中目标识别过程的论述, 总结目前基于被动声呐音频信号的水中目标自动识别算法存在的困难与挑战, 并对该领域未来的发展方向进行展望.
- 被动声呐信号 /
- 水中目标自动识别 /
- 深度学习 /
- 有监督学习 /
- 自监督学习
Abstract: Underwater target recognition based on passive sonar acoustic signals is a significant technical challenge in the field of underwater unmanned detection, with broad applications in both military and civilian domains. This paper provides a comprehensive exposition of the methodology and process involved in underwater target recognition using passive sonar acoustic signals, addressing data processing and recognition methods at two levels. Regarding data processing, this paper presents a thorough exploration of the fundamental principles of passive sonar signal processing from three key aspects: The underlying process of underwater target recognition based on passive sonar signals, the mathematical foundations of passive sonar acoustic signal analysis, and the extraction of relevant features. At the level of recognition methods, this paper offers a comprehensive analysis of underwater target recognition techniques based on machine learning algorithms, and focus on the research conducted with deep learning algorithms at its core. The paper systematically summarizes and analyzes the current research progress across various learning paradigms, including supervised learning, unsupervised learning and self-supervised learning, and analyzes their advantages and disadvantages in terms of the algorithm＇s labeled data requirement, robustness, scalability and adaptability. Additionally, this paper provides an overview of widely-used public datasets in the field, and outlines the essential elements that such datasets should possess. Finally, by discussing the process of underwater target recognition, this paper summarizes the current difficulties and challenges in automatic underwater target recognition algorithms based on passive sonar acoustic signals, and offers insights into the future development direction of this field.
- Passive sonar signal /
- automatic underwater target recognition /
- deep learning /
- supervised learning /
- self-supervised learning

HTML全文

图 1 基于机器学习的水声目标识别方法

Fig. 1 Machine learning-based methods for UATR

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图 2 基于声呐信号的水声目标识别基本原理

Fig. 2 Fundamental principles of UATR based on sonar signals

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图 3 水声目标识别的基本流程

Fig. 3 Basic procedure of UATR

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图 4 被动声呐信号的特征图示例

Fig. 4 The illustrative feature examples of passive sonar signals

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图 5 基于深度学习的水中音频信号特征提取范式

Fig. 5 Deep learning-based paradigm for underwater acoustic signals feature extraction

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图 6 基于深度学习的水声目标识别主流算法模型发展时间轴线

Fig. 6 Timeline: Evolution of mainstream deep learning algorithms for UATR

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图 7 基于CNN的水声目标识别方法基本架构

Fig. 7 Basic framework of CNN-based methods for UATR

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图 8 基于CNN的水声目标识别主流优化方法

Fig. 8 Mainstream optimization methods for CNN-based UATR

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图 9 基于CNN与Bi-LSTM融合的水声目标识别方法网络架构

Fig. 9 Network framework of UATR methods based on the fusion of CNN and Bi-LSTM

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图 10 基于Transformer的水声目标识别方法基本架构

Fig. 10 Basic framework of Transformer-based methods for UATR

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图 11 基于RBM自编码器重构的水声目标识别方法架构

Fig. 11 The framework of RBM autoencoder-based reconstruction methods for UATR

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图 12 SSAST的网络结构

Fig. 12 The network architecture of SSAST

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图 13 不同深度学习方法在水声目标识别领域的性能对比

Fig. 13 Performance comparison of various deep learning methods for UATR

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表 1 典型传统机器学习的水声目标识别算法

Table 1 Typical traditional machine learning algorithms for UATR

年份	机器学习算法	音频特征	数据集
1992	Naive Bayes^[54]	目标固有物理机理特征	自建数据集
2016	DT^[55]	时域、频域特征	仿真数据集
2014		基于小波变换的时频特征	自建数据集
2016		MFCC	真实数据集
2017		GFCC	历史数据集
2017	SVM^[56−62]	改进的GFCC	舰船数据集
2018		过零率	鱼类数据集
2019		融合表征	自建数据集
2022		LOFAR谱	ShipsEar
2018	KNN^{[60, 62]}	MFCC	鱼类数据集
2022	KNN^{[60, 62]}	LOFAR谱	ShipsEar
2011	SVDD^[63]	—	舰船数据集
2014	HMM^{[56, 64]}	MFCC	—
2018	HMM^{[56, 64]}	MFCC	机器音频数据集

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表 2 基于卷积神经网络的水声目标识别方法

Table 2 Convolutional neural network-based methods for UATR

年份	技术特点	模型优劣分析	数据集来源	样本大小
2017	卷积神经网络^[66]	自动提取音频表征, 提高了模型的精度	Historic Naval Sound and Video database	16类
2018	卷积神经网络^[71]	使用极限学习机代替全连接层, 提高了模型的识别精度	私有数据集	3类
2019	卷积神经网络^[70]	使用二阶池化策略, 更好地保留了信号分量的差异性	中国南海	5类
2019	一种基于声音生成感知机制的卷积神经网络^[41]	模拟听觉系统实现多尺度音频表征学习, 使得表征更具判别性	Ocean Networks Canada	4类
2020	基于ResNet的声音生成感知模型^[42]	使用全局平均池化代替全连接层, 极大地减少了参数, 提高了模型的训练效率	Ocean Networks Canada	4类
2020	一种稠密卷积神经网络DCNN^[43]	使用DCNN自动提取音频特征, 降低了人工干预对性能的影响	私有数据集	12类
2021	一种具有稀疏结构的GoogleNet^[72]	稀疏结构的网络设计减少了参数量, 提升模型的训练效率	仿真数据集	3类
	一种基于可分离卷积自编码器的SCAE模型^[73]	使用音频的融合表征进行分析, 证明了方法的鲁棒性	DeepShip	5类
	残差神经网络^[76]	融合表征使得学习到的音频表征更具判别性, 提升了模型的性能	ShipsEar	5类
	基于注意力机制的深度神经网络^[46]	使用注意力机制抑制了海洋环境噪声和其他舰船信号的干扰, 提升模型的识别能力	中国南海	4类
	基于双注意力机制和多分辨率卷积神经网络架构^[81]	多分辨率卷积网络使得音频表征更具判别性, 双注意力机制有利于同时关注局部信息与全局信息	ShipsEar	5类
	基于多分辨率的时频特征提取与数据增强的水中目标识别方法^[85]	多分辨率卷积网络使得音频表征更具判别性, 数据增强增大了模型的训练样本规模, 从而提升了模型的识别性能	ShipsEar	5类
2022	基于通道注意力机制的残差网络^[82]	通道注意力机制的使用使得学习到的音频表征更具判别性和鲁棒性, 提升了模型的性能	私有数据集	4类
2022	一种基于融合表征与通道注意力机制的残差网络^[83]	融合表征与通道注意力机制的使用使得学习到的音频表征更具判别性和鲁棒性, 提升了模型的性能	DeepShip ShipsEar	5类
2023	基于注意力机制的多分支CNN^[74]	注意力机制用以捕捉特征图中的重要信息, 多分支策略的使用提升了模型的训练效率	ShipsEar	5类

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表 3 基于时延神经网络、循环神经网络和Transformer的水声目标识别方法

Table 3 Time delay neural networks-based, recurrent neural network-based and Transformer-based methods for UATR

年份	技术特点	模型优劣分析	数据集来源	样本大小
2019	基于时延神经网络的UATR^[87]	时延神经网络能够学习音频时序信息, 从而提高模型的识别能力	私有数据集	2类
2022	一种可学习前端^[88]	可学习的一维卷积滤波器可以实现更具判别性的音频特征提取, 表现出比传统手工提取的特征更好的性能	QLED, ShipsEar, DeepShip	QLED 2类ShipsEar 5类DeepShip 4类
2020	采用Bi-LSTM同时考虑过去与未来信息的UATR^[89]	使用双向注意力机制能够同时学习到历史和后续的时序信息, 从而使得音频表征蕴含信息更丰富以及判别性更高, 然而该方法复杂度较高	Sea Trial	2类
2020	基于Bi-GRU的混合时序网络^[90]	混合时序网络从多个维度关注时序信息, 从而学习到更具判别性的音频特征, 提高模型的识别能力	私有数据集	3类
2021	采用LSTM融合音频表征^[91]	该方法能够同时学习音频的相位和频谱特征, 并加以融合, 从而提升模型的识别性能	私有数据集	2类
2021	CNN与Bi-LSTM组合的UATR^[92]	CNN与Bi-LSTM组合可以提取出同时关注局部特性和时序上下文依赖的音频特征, 提高了模型的识别能力	私有数据集	3类
2022	一维卷积与LSTM组合的UATR^[93]	首次采用一维卷积和LSTM的组合网络提取音频表征, 能够在提高音频识别率的同时降低模型的参数量, 然而该方法稳定性有待提高	ShipsEar	5类
2022	Transformer^[94−95]	增强了模型的泛化性和学习能力, 提高了模型的识别准确率	ShipsEar	5类
2022	Transformer^[94−95]	加入逐层聚合的Token机制, 同时兼顾全局信息和局部特性, 提高了模型的识别准确率	ShipsEar, DeepShip	5类

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表 4 基于迁移学习的水声目标识别方法

Table 4 Transfer learning-based methods for UATR

年份	技术特点	模型优劣分析	数据集来源	样本大小
2019	基于ResNet的迁移学习^[98]	在保证较高性能的同时减少对标签样本的需求, 但不同领域任务的数据特征分布存在固有偏差	Whale FM website	16类
2020	基于ResNet的迁移学习^[99]	在预训练模型的基础上设计模型集成机制, 提升识别性能的同时减少了对标签样本的需求, 但不同领域任务的数据特征分布存在固有偏差	私有数据集	2类
2020	基于CNN的迁移学习^[102]	使用AudioSet音频数据集进行预训练, 减轻了不同领域任务的数据特征分布所存在的固有偏差	—	—
2022	基于VGGish的迁移学习^[103]	除了使用AudioSet数据集进行预训练, 还设计基于时频分析与注意力机制结合的特征提取模块, 提高了模型的泛化能力	ShipsEar	5类

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表 5 基于无监督学习和自监督学习的水声目标识别方法

Table 5 Unsupervised and self-supervised learning-based methods for UATR

年份	技术特点	模型优劣分析	数据集来源	样本大小
2013	深度置信网络^[104−108]	对标注数据集的需求小, 但由于训练数据少, 容易出现过拟合的风险	私有数据集	40类
2017		加入混合正则化策略, 增强了所学到音频表征的判别性, 提高了模型的识别准确率		3类
2018		加入竞争机制, 增强了所学到音频表征的判别性, 提高了模型的识别准确率		2类
2018		加入压缩机制, 减少了模型的冗余参数, 提升了模型的识别准确率	中国南海	2类
2021	基于RBM自编码器与重构的SSL^[111]	降低了模型对标签数据的需求, 增强了模型的泛化性和可扩展性	ShipsEar	5类
2022	基于掩码建模与重构的SSL^[113]	使用掩码建模与多表征重构策略, 提升了模型对特征的学习能力, 从而提升了识别性能	DeepShip	5类
2023	基于自监督对比学习的SSL^[112]	降低了模型对标签数据的需求, 增强了学习到的音频特征的泛化性和对数据的适应能力	ShipsEar, DeepShip	5类

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表 6 常用的公开水声数据集总结

Table 6 Summary of commonly used public underwater acoustic signal datasets

数据集名称	数据结构				数据类型	采样频率 (kHz)	获取地址
数据集名称	类别	样本数 (个)	持续时间 (样本)	总时间	数据类型	采样频率 (kHz)	获取地址
DeepShip	Cargo Ship	110	180 ~ 610 s	10 h 40 min	音频	32	DeepShip
	Tug	70	180 ~ 1140 s	11 h 17 min
	Passenger Ship	70	6 ~ 1530 s	12 h 22 min
	Tanker	70	6 ~ 700 s	12 h 45 min
ShipsEar	A	16	—	1729 s	音频	32	ShipsEar
	B	17		1435 s
	C	27		4054 s
	D	9		2041 s
	E	12		923 s
Ocean Network Canada (ONC)	Background noise	17000	3 s	8 h 30 min	音频	—	ONC
	Cargo	17000		8 h 30 min
	Passenger Ship	17000		8 h 30 min
	Pleasure craft	17000		8 h 30 min
	Tug	17000		8 h 30 min
Five-element acoustic dataset	9个类别	360	0.5 s	180 s	音频	—	Five-element···
Historic Naval Sound and Video	16个类别	—	2.5 s	—	音视频	10.24	Historic Naval···
DIDSON	8个类别	524	—	—	—	—	DIDSON
Whale FM	Pilot whale	10858	1 ~ 8 s	5 h 35 min	音频	—	Whale FM
Whale FM	Killer whale	4673	1 ~ 8 s	5 h 35 min	音频	—	Whale FM
注: 1. 官方给出的DeepShip数据集只包含Cargo Ship、Tug、Passenger Ship和Tanker这4个类别. 在实际研究中, 学者通常会自定义一个新的类别 “Background noise”作为第5类. 2. 获取地址的访问时间为2023-07-20.

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