基于改进1DCNN+TCN的雷达辐射源快速识别方法

TN971.1; 为了解决传统雷达辐射源识别方式识别速度慢、在低信噪比时很难准确识别等问题,结合深度学习提出了一种基于改进一维卷积神经网络(one-dimensional convolutional neural network,1DCNN)和时间卷积网络(temporal convolutional network,TCN)的雷达辐射源快速识别模型.在1DCNN的基础上加入了批归一化层,并在全连接层前加入注意力机制;同时在原有TCN的基础上进行改进,使用Leaky ReLU激活函数代替ReLU函数;将改进后的TCN与1DCNN相连接.仿真实验结果分析表明,该模型不仅能够迅速识别出辐射源信号,...

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Published in系统工程与电子技术 Vol. 44; no. 2; pp. 463 - 469
Main Authors 金涛, 王晓峰, 田润澜, 张歆东
Format Journal Article
LanguageChinese
Published 吉林大学电子科学与工程学院,吉林长春130012%空军航空大学航空作战勤务学院,吉林长春130022 01.02.2022
Subjects
辐射源信号快速识别;时间序列;时间卷积网络;一维卷积神经网络;参数化线性修正单元;注意力机制
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ISSN1001-506X
DOI10.12305/j.issn.1001-506X.2022.02.14

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Abstract TN971.1; 为了解决传统雷达辐射源识别方式识别速度慢、在低信噪比时很难准确识别等问题,结合深度学习提出了一种基于改进一维卷积神经网络(one-dimensional convolutional neural network,1DCNN)和时间卷积网络(temporal convolutional network,TCN)的雷达辐射源快速识别模型.在1DCNN的基础上加入了批归一化层,并在全连接层前加入注意力机制;同时在原有TCN的基础上进行改进,使用Leaky ReLU激活函数代替ReLU函数;将改进后的TCN与1DCNN相连接.仿真实验结果分析表明,该模型不仅能够迅速识别出辐射源信号,识别准确率也较高,能够有效平衡模型识别速度和识别精度.
AbstractList TN971.1; 为了解决传统雷达辐射源识别方式识别速度慢、在低信噪比时很难准确识别等问题,结合深度学习提出了一种基于改进一维卷积神经网络(one-dimensional convolutional neural network,1DCNN)和时间卷积网络(temporal convolutional network,TCN)的雷达辐射源快速识别模型.在1DCNN的基础上加入了批归一化层,并在全连接层前加入注意力机制;同时在原有TCN的基础上进行改进,使用Leaky ReLU激活函数代替ReLU函数;将改进后的TCN与1DCNN相连接.仿真实验结果分析表明,该模型不仅能够迅速识别出辐射源信号,识别准确率也较高,能够有效平衡模型识别速度和识别精度.
Author 金涛
田润澜
张歆东
王晓峰
AuthorAffiliation 吉林大学电子科学与工程学院,吉林长春130012%空军航空大学航空作战勤务学院,吉林长春130022
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Author_FL WANG Xiaofeng
JIN Tao
ZHANG Xindong
TIAN Runlan
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Keywords 辐射源信号快速识别;时间序列;时间卷积网络;一维卷积神经网络;参数化线性修正单元;注意力机制
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Snippet TN971.1; 为了解决传统雷达辐射源识别方式识别速度慢、在低信噪比时很难准确识别等问题,结合深度学习提出了一种基于改进一维卷积神经网络(one-dimensional convolutional...
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Title 基于改进1DCNN+TCN的雷达辐射源快速识别方法
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