基于循环生成对抗网络的增强罗兰信号生成  

Enhanced LORAN signals generating based on cycle-consistent adversarial networks

作　　者：李辉 胡登峰 张恺 邹波蓉 刘薇 Li Hui;Hu Dengfeng;Zhang Kai;Zou Borong;Liu Wei(School of Pnysics and Electronic lnformation Engineering,Henan Polytechnic University,Jiaozuo 454000,China)

机构地区：[1]河南理工大学物理与电子信息学院,焦作454000

出　　处：《电子测量技术》2024年第6期164-172,共9页Electronic Measurement Technology

基　　金：河南省科技攻关计划(232102211005);河南理工大学博士基金(B2022-4)项目资助。

摘　　要：在信号生成算法中,需要大量标记信号样本用于网络训练,但通常携带电文信息标记的信号难以批量获取。针对此问题本文提出一种基于循环生成对抗网络和迁移学习的方法,实现了无需大量信号及对应电文作为标记的增强罗兰信号生成,并使用迁移学习在少量实测信号情况下快速生成。循环生成对抗网络的结构包括两个生成器和两个判别器,利用无需一一对应的增强罗兰信号和电文数据集,使生成器学习到两个数据集之间的相互转换关系,实现输入电文数据可以生成与之相对应的增强罗兰信号,并且针对增强罗兰信号的特性,使用一维卷积、残差网络、自注意力机制对网络模型进行改进。实验证实,生成信号与实测数据的均方误差为0.0153,平均皮尔逊相关系数为0.9843,且所含电文信息准确率为99.02%。本文在PSK、ASK、FSK数据集上验证了算法,实验结果表明生成的信号满足预期,为未知参数的信号调制和解调提供一种新的思路。In signal generation algorithms,a large number of labeled signal samples are needed for network training,but it is usually difficult to obtain signals carrying message information markers in bulk.To address this problem,this paper proposes a method based on CycleGAN and transfer learning,which realizes the generation of Enhanced LORAN signals without the need for a large number of signals and the corresponding messages as markers and uses migration learning to generate them quickly with a small number of measured signals.The structure of the CycleGAN includes two generators and two discriminators,using the Enhanced LORAN signals and message data sets that do not need to be one-to-one correspondence,so that the generator learns the interconversion relationship between the two data sets,and realises that the input message data can generate the Enhanced LORAN signals corresponding to it,for the characteristics of the Enhanced LORAN signal,the network model is improved using a one-dimensional convolution,residual network,and self-attention mechanism.Experimentally confirmed,it is confirmed that the mean square error of the signal generated by this paper with the measured data is 0.0153,the average Pearson correlation coefficient is 0.9843,and the accuracy of the contained message information is 99.02%.To verify the universality of the algorithm,this paper validates the algorithm on PSK,ASK,and FSK datasets,and the experimental results show that the generated signals satisfy the expectations and provide a new idea for signal modulation and demodulation with unknown parameters.

关 键 词：信号生成 循环生成对抗网络 迁移学习 增强罗兰信号 

分 类 号：TN911.7[电子电信—通信与信息系统]