基于深度残差适配网络的通信辐射源个体识别

TN911.7; 为解决通信辐射源识别中传统的人工特征提取方法鲁棒性不足和深度学习方法需要大量带标签目标域数据的问题,提出一种基于深度残差适配网络的通信辐射源个体识别方法.应用深度学习技术实现从源域到目标域上的迁移识别,只需要将带标签的源域数据和无标签的目标域数据进行训练.原始通信辐射源信号经过预处理后输入网络训练,将源域和目标域的分布差异和网络的损失函数作为优化目标,反复迭代得到最终模型.在实际采集的通信辐射源数据集上的实验结果证明了该方法的可行性和有效性....

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Published in系统工程与电子技术 Vol. 43; no. 3; pp. 603 - 609
Main Authors 陈浩, 杨俊安, 刘辉
Format Journal Article
LanguageChinese
Published 国防科技大学电子对抗学院,安徽合肥230037 01.03.2021
Subjects
特征提取
辐射源个体识别
迁移学习
深度学习
Online AccessGet full text
ISSN1001-506X
DOI10.12305/j.issn.1001-506X.2021.03.02

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Abstract TN911.7; 为解决通信辐射源识别中传统的人工特征提取方法鲁棒性不足和深度学习方法需要大量带标签目标域数据的问题,提出一种基于深度残差适配网络的通信辐射源个体识别方法.应用深度学习技术实现从源域到目标域上的迁移识别,只需要将带标签的源域数据和无标签的目标域数据进行训练.原始通信辐射源信号经过预处理后输入网络训练,将源域和目标域的分布差异和网络的损失函数作为优化目标,反复迭代得到最终模型.在实际采集的通信辐射源数据集上的实验结果证明了该方法的可行性和有效性.
AbstractList TN911.7; 为解决通信辐射源识别中传统的人工特征提取方法鲁棒性不足和深度学习方法需要大量带标签目标域数据的问题,提出一种基于深度残差适配网络的通信辐射源个体识别方法.应用深度学习技术实现从源域到目标域上的迁移识别,只需要将带标签的源域数据和无标签的目标域数据进行训练.原始通信辐射源信号经过预处理后输入网络训练,将源域和目标域的分布差异和网络的损失函数作为优化目标,反复迭代得到最终模型.在实际采集的通信辐射源数据集上的实验结果证明了该方法的可行性和有效性.
Author 杨俊安
陈浩
刘辉
AuthorAffiliation 国防科技大学电子对抗学院,安徽合肥230037
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LIU Hui
YANG Jun'an
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辐射源个体识别
迁移学习
深度学习
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