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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

徐齐胜, 许可乐, 窦勇, 高彩丽, 乔鹏, 冯大为, 朱博青. 基于被动声呐音频信号的水中目标识别综述. 自动化学报, 2024, 50(4): 649−673 doi: 10.16383/j.aas.c230153
引用本文: 徐齐胜, 许可乐, 窦勇, 高彩丽, 乔鹏, 冯大为, 朱博青. 基于被动声呐音频信号的水中目标识别综述. 自动化学报, 2024, 50(4): 649−673 doi: 10.16383/j.aas.c230153
Xu Qi-Sheng, Xu Ke-Le, Dou Yong, Gao Cai-Li, Qiao Peng, Feng Da-Wei, Zhu Bo-Qing. A review of underwater target recognition based on passive sonar acoustic signals. Acta Automatica Sinica, 2024, 50(4): 649−673 doi: 10.16383/j.aas.c230153
Citation: Xu Qi-Sheng, Xu Ke-Le, Dou Yong, Gao Cai-Li, Qiao Peng, Feng Da-Wei, Zhu Bo-Qing. A review of underwater target recognition based on passive sonar acoustic signals. Acta Automatica Sinica, 2024, 50(4): 649−673 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
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

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

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: 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.
  • 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

    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

    图  3  水声目标识别的基本流程

    Fig.  3  Basic procedure of UATR

    图  4  被动声呐信号的特征图示例

    Fig.  4  The illustrative feature examples of passive sonar signals

    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

    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

    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

    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

    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

    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

    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

    图  12  SSAST的网络结构

    Fig.  12  The network architecture of SSAST

    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

    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
    1992Naive Bayes[54]目标固有物理机理特征自建数据集
    2016DT[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
    2016MFCC真实数据集
    2017GFCC历史数据集
    2017SVM[5662]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
    2022LOFAR谱ShipsEar
    2018KNN[60, 62]MFCC鱼类数据集
    2022LOFAR谱ShipsEar
    2011SVDD[63]舰船数据集
    2014HMM[56, 64]MFCC
    2018机器音频数据集
    下载: 导出CSV

    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 database16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
    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 Canada4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 Canada4类
    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DeepShip5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ShipsEar5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ShipsEar5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ShipsEar5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
    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 ShipsEar5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ShipsEar5类
    下载: 导出CSV

    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 Trial2类
    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
    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ShipsEar5类
    2022Transformer[9495]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ShipsEar5类
    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
    下载: 导出CSV

    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 website16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
    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ShipsEar5类
    下载: 导出CSV

    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[104108]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ShipsEar5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DeepShip5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, DeepShip5类
    下载: 导出CSV

    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
    类别样本数 (个)持续时间 (样本)总时间
    DeepShipCargo Ship110180 ~ 610 s10 h 40 min音频32DeepShip
    Tug70180 ~ 1140 s11 h 17 min
    Passenger Ship706 ~ 1530 s12 h 22 min
    Tanker706 ~ 700 s12 h 45 min
    ShipsEarA161729 s音频32ShipsEar
    B171435 s
    C274054 s
    D92041 s
    E12923 s
    Ocean Network Canada (ONC)Background noise170003 s8 h 30 min音频ONC
    Cargo170008 h 30 min
    Passenger Ship170008 h 30 min
    Pleasure craft170008 h 30 min
    Tug170008 h 30 min
    Five-element acoustic dataset9个类别3600.5 s180 s音频Five-element···
    Historic Naval Sound and Video16个类别2.5 s音视频10.24Historic Naval···
    DIDSON8个类别524DIDSON
    Whale FMPilot whale108581 ~ 8 s5 h 35 min音频Whale FM
    Killer whale4673
    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.
    下载: 导出CSV
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  • 收稿日期:  2023-03-22
  • 录用日期:  2023-07-10
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