基于GAN的通信干扰波形生成技术

doi:10.12305/j.issn.1001-506X.2021.04.26

摘要/Abstract

摘要：

现有通信干扰方法, 通常基于通信侦察中获取的目标信号特征进行干扰决策, 选取合适的干扰波形实施干扰, 难以应对目标信号特征未知或参数动态变化的情况。为此, 提出一种基于生成对抗网络(generative adversarial networks, GAN)的通信干扰波形生成技术, 运用GAN直接提取目标信号的潜在特征, 并生成与目标信号特征相似的干扰波形。在介绍GAN原理的基础上, 首先设计网络模型, 并对学习率进行优化, 使GAN更适用于时间序列通信干扰波形的生成。然后通过对不同类型和参数的通信信号进行干扰波形生成实验, 验证了该技术的泛化性。最后进行干扰效果对比试验, 结果表明, GAN生成的干扰波形干扰效果能够逼近最佳干扰效果。

关键词: 通信干扰波形生成, 机器学习, 生成对抗网络

Abstract:

The traditional communication jamming methods usually use fixed jamming wave forms and make jamming decisions based on target signals features obtained during the communication reconnaissance, Which is difficult to deal with the situation that the target signal characteristics are unknown or the parameters change dynamically. An communication jamming waveform generation technology based on generative adversarial networks (GAN) is proposed to solve these problems. GAN are used to extract the potential features of target signal and generate the jamming waveform with the same features as the target signal. Firstly, Based on the introduction of GAN principle, the network design and learning rate optimization experiment are carried out to make GAN more suitable for the generation of time series communication jamming waveform. Then, the generalization experiment is carried out on communication signals with different types and parameters, and results showed that this technology works well on other communication signals. Finally, the comparision experiment of jamming effect of generated jamming waveform, Gaussian noise and optimal jamming waveform is carried out, and results showed that the jamming effect of generated jamming waveform can approximate the jamming effect of optimal jamming waveform when the jamming to signal ratio increases gradually.

Key words: communication jamming waveform generation, machine learning, generative adversarial networks (GAN)

中图分类号:

TN975

赵凡, 金虎. 基于GAN的通信干扰波形生成技术[J]. 系统工程与电子技术, 2021, 43(4): 1080-1088.

Fan ZHAO, Hu JIN. Communication jamming waveform generation technology based on GAN[J]. Systems Engineering and Electronics, 2021, 43(4): 1080-1088.

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