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基于时-频注意力机制网络的水声目标线谱增强

古天龙 张清智 李晶晶

古天龙, 张清智, 李晶晶. 基于时-频注意力机制网络的水声目标线谱增强[J]. 电子与信息学报, 2024, 46(1): 92-100. doi: 10.11999/JEIT230217
引用本文: 古天龙, 张清智, 李晶晶. 基于时-频注意力机制网络的水声目标线谱增强[J]. 电子与信息学报, 2024, 46(1): 92-100. doi: 10.11999/JEIT230217
GU Tianlong, ZHANG Qingzhi, LI Jingjing. Line Spectrum Enhancement of Underwater Acoustic Targets Based on a Time-Frequency Attention Network[J]. Journal of Electronics & Information Technology, 2024, 46(1): 92-100. doi: 10.11999/JEIT230217
Citation: GU Tianlong, ZHANG Qingzhi, LI Jingjing. Line Spectrum Enhancement of Underwater Acoustic Targets Based on a Time-Frequency Attention Network[J]. Journal of Electronics & Information Technology, 2024, 46(1): 92-100. doi: 10.11999/JEIT230217

基于时-频注意力机制网络的水声目标线谱增强

doi: 10.11999/JEIT230217
基金项目: 国家自然科学基金(U22A2099, 62172350),中央高校基本科研业务费专项资金(21621028),广州市科技计划项目(202201011128)
详细信息
    作者简介:

    古天龙:男,教授,主要研究方向为深度学习、数据治理、人工智能安全

    张清智:男,硕士生,主要研究方向为水声信号处理、深度学习

    李晶晶:女,讲师,主要研究方向为智能信号处理、深度学习、水声信号处理

    通讯作者:

    李晶晶 lijingjing@jnu.edu.cn

  • 中图分类号: TN911.7; TB535

Line Spectrum Enhancement of Underwater Acoustic Targets Based on a Time-Frequency Attention Network

Funds: The National Natural Science Foundation of China (U22A2099, 62172350), Fundamental Research Foundation for the Central Universities (21621028), Science and Technology Projects in Guangzhou (202201011128)
  • 摘要: 为提高被动声纳对水下低噪声安静型目标的检测,研究者开始关注基于深度学习的线谱增强方法,其中,基于LSTM的线谱增强网络由于同时具有时域和频域的非线性处理能力,具有很强的灵活性,然而其性能还需要进一步提升。为此,该文提出了基于时-频注意力机制的网络模型(TFA-Net),通过在LSTM模型的基础上同时增加时域注意力机制和频域注意力机制,充分利用了目标信号在时域和频域的双重重要特征,提升了对LOFAR谱的线谱增强效果。TFA-Net中的时域注意力机制利用LSTM隐藏状态之间的关联性,增加了模型在时域的注意力,频率注意力机制通过将深度残差收缩网络中收缩子网络的全链接层设计为1维卷积层,增加了模型在频域的注意力。相比于LSTM,TFA-Net具有更高的系统信噪比增益:在输入信噪比为–3 dB的情况下,将系统信噪比增益由2.17 dB提升到12.56 dB;在输入信噪比为–11 dB的情况下,将系统信噪比增益由0.71 dB提升到10.6 dB。仿真和实测数据的实验结果表明,TFA-Net可以有效提升LOFAR谱的线谱增强效果,解决低信噪比下水下目标的检测问题。
  • 图  1  技术路线图

    图  2  DLE和基于LSTM的线谱增强模型结构

    图  3  基于时-频注意力机制的线谱增强网络模型结构

    图  4  时域注意力机制模块结构

    图  5  残差卷积收缩模块结构

    图  6  高斯白噪声实验结果

    图  7  输入信噪比为–3 dB时的线谱增强结果

    图  8  不同输入信噪比下系统信噪比增益对比曲线

    图  9  不同网络深度下的线谱增强效果对比

    图  10  实测数据实验结果

    表  1  TFA-Net模型超参数设置

    模块参数名称参数值
    LSTM隐藏层神经元个数200
    频域注意力机制模块残差卷积收缩模块个数10
    全局参数激活函数(activate function)ReLU
    损失函数(loss function)MSE
    优化器(optimizer)Adam
    迭代次数(epoch)200
    学习率(learning rate)0.0015
    下载: 导出CSV

    表  2  输入信噪比为–3 dB时的系统信噪比增益对比

    线谱增强效果量化指标训练前LSTM训练后TFA-Net训练后
    图像信噪比(dB)5.048.339490.92
    系统信噪比增益(dB)2.1712.56
    下载: 导出CSV

    表  3  消融实验结果

    模型–3 dB–5 dB–7 dB–9 dB–11 dB
    TFA-Net12.5611.8411.4711.1010.60
    (w/o)时域注意力11.4611.1910.5510.479.99
    (w/o)卷积改进10.8410.7010.549.188.35
    (w/o)时域注意力&卷积改进10.299.568.708.107.74
    (w/o)频域注意力6.555.685.344.453.59
    (w/o)时域&频域注意力2.171.681.450.730.71
    注:TFA-Net包含LSTM、时域注意力机制模块及频域注意力机制模块,其中频域注意力机制模块包含深度残差收缩模块和卷积改进模块。
    下载: 导出CSV
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出版历程
  • 收稿日期:  2023-04-03
  • 修回日期:  2023-08-28
  • 网络出版日期:  2023-08-30
  • 刊出日期:  2024-01-17

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