基于SABEGAN的通信干扰信号生成与效能分析  

Generation and efficiency analysis of communication jamming signal based on SABEGAN

作　　者：薛丽莎 葛瑞星 朱宇轩 鲍雁飞 XUE Lisha;GE Ruixing;ZHU Yuxuan;BAO Yanfei(Unit 32802 of the PLA,Beijing 100083,China)

机构地区：[1]中国人民解放军32802部队,北京100083

出　　处：《系统工程与电子技术》2024年第6期2155-2163,共9页Systems Engineering and Electronics

基　　金：国家自然科学基金(61971440)资助课题 .

摘　　要：针对复杂电磁环境下,传统电子干扰方法对目标信号识别困难、干扰效能弱化等问题,提出了一种基于自注意力边界平衡生成对抗网络的干扰信号生成模型。所提模型利用上采样模块来增强生成数据的适应度,引入自注意力机制来兼顾信号特征提取的局部性与全局性,既使结果更精准,又降低了计算复杂度。同时,利用基于自编码器架构的判别器来促进模型快速稳定地收敛。实验结果表明,该模型能对非合作目标信号进行自适应识别与学习,自动生成对应的干扰信号,且干扰效能优于传统干扰算法及经典生成对抗网络模型算法,为基于机器学习的通信对抗技术提供了新的研究思路。Aiming at the problems of difficult target signal identification and weakened jamming effectiveness of traditional electronic jamming methods in complex electromagnetic environment,a jamming signal generation model based on self-attentive boundary-balanced generative adversarial network is proposed.The proposed model uses the up-sampling module to enhance the fineness of the generated data,and introduces the self-attention mechanism to take into account the local and global nature of the signal feature extraction,which not only makes the results more accurate,but also reduces the computational complexity.A discriminator based on the self-encoder architecture is also used to facilitate the fast and stable convergence of the model.The experimental results show that the proposed model can adaptively identify and learn the non-cooperative target signals and automatically generate the corresponding jamming signals,and the jamming efficiency is better than the traditional jamming algorithms and the classical generative adversarial network model algorithms,which provides a new research idea for the communication adversarial technology based on machine learning.

关 键 词：通信对抗 生成对抗网络 自注意力机制 自编码器 信号生成 

分 类 号：TN975[电子电信—信号与信息处理]