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。
Deep learning-based line spectrum enhancement methods have received increasing attention for improving the detection performance of underwater low-noise targets using passive sonar.Among them,Long Short-Term Memory(LSTM)-based line spectrum enhancement networks have high flexibility due to their nonlinear processing capabilities in time and frequency domains.However,their performance requires further improvement.Therefore,a Time-Frequency Attention Network(TFA-Net)is proposed herein.The line spectrum enhancement effect of the LOw-Frequency Analysis Record(LOFAR)spectrum can be improved by incorporating the time and frequency-domain attention mechanisms into LSTM networks,In TFA-Net,the time-domain attention mechanism utilizes the correlation between the hidden states of LSTM to increase the model’s attention in the time domain,while the frequency-domain attention mechanism increases the model’s attention in the frequency domain by designing the full link layer of the shrinkage sub-network in deep residual shrinkage networks as a one-dimensional convolutional layer.Compared to LSTM,TFA-Net has a higher system signal-to-noise ratio gain:when the input signal-to-noise ratio is–3 dB and–11 dB,the system signal-to-noise ratio gain is increased from 2.17 to 12.56 dB and from 0.71 to 10.6 dB,respectively.Experimental results based on simulated and real data show that TFA-Net could effectively improve the line spectrum enhancement effect of the LOFAR spectrum and address the problem of detecting underwater low-noise targets.
作者
古天龙
张清智
李晶晶
GU Tianlong;ZHANG Qingzhi;LI Jingjing(Engineering Research Center of Trustworthy AI,Ministry of Education,Jinan University,Guangzhou 510632,China;Guangxi Key Laboratory of Trusted Software,Guilin University of Electronic Technology,Guilin 541004,China)
出处
《电子与信息学报》
EI
CAS
CSCD
北大核心
2024年第1期92-100,共9页
Journal of Electronics & Information Technology
基金
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)。
