面向电磁信息智能控制的生成对抗网络研究进展

doi:10.12305/j.issn.1001-506X.2025.03.06

Abstract

Abstract:

Electromagnetic information intelligent control is a key technology for managing and utilizing the electromagnetic environment in modern warfare, with the observe-orient-decide-act (OODA) loop providing theoretical guidance for this process. Generative adversarial network (GAN) and its derivative models, with their outstanding data generation and adaptation capabilities, significantly enhance the observation and analysis ability of electromagnetic environment information, bringing new momentum to the intelligence of the OODA loop in electromagnetic spectrum warfare. This paper delves into the application of GAN and their derivatives in the OODA loop of electromagnetic spectrum warfare, particularly how they enhance cognitive efficacy in key aspects such as signal detection and identification, specific emitter identification, and strategy optimization. Additionally, it discusses the challenges GAN face in this field, such as data quality and model generalization capabilities, aiming to promote in-depth research and application of this technology in the field of electromagnetic information intelligent control, thereby fostering technological innovation and development.

Key words: electromagnetic information intelligent control, generative adversarial network (GAN), observe-orient-decide-act (OODA) loop, signal recognition, strategy optimization

CLC Number:

TN975

Lan ZHANG, Biao ZHANG, Tianyi LIANG, Huijie ZHU. Research progress on generative adversarial network for electromagnetic information intelligent control[J]. Systems Engineering and Electronics, 2025, 47(3): 730-744.

Figures/Tables 9

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GAN衍生模型名称	改进的技术手段	优势	劣势
CGAN	引入额外的条件信息	提高生成任务的灵活性和可控性	训练不稳定, 对于复杂、高维的条件变量处理能力有限
DCGAN	使用CNN作为基础结构, 使用非线性激活函数和批量标化技术	提高了模型的非线性拟合能力和可扩展性	计算资源需求高, 训练过程稳定性较差
InfoGAN	引入可解释的隐变量, 目标函数最大化隐变量和生成数据之间的互信息	实现可控生成, 增强模型的可解释性, 适用于无监督学习场景	训练过程缺乏稳定性, 隐变量解释具有主观性, 互信息估算不精确
ACGAN	引入辅助分类器, 同时进行生成任务和分类任务	提升生成样本的多样性和分类准确性	训练稳定性较差, 计算资源和时间需求高, 条件约束选择要求较高
WGAN	使用Wasserstein距离作为损失函数	提高训练过程的稳定性, 可定量评价生成模型性能	K-Lipschitz约束实现复杂, 在大规模数据集上计算资源要求较高
BiGAN	引入额外的编码器结构, 将真实数据分布映射到潜在噪声域	结构相对简单, 易于理解和实现; 双向学习机制提高特征提取和表示学习能力	引入编码器增加了训练的复杂性, 需要较多的计算资源
LSGAN	使用最小平方损失函数替代交叉熵损失	缓解梯度消失问题, 提高训练稳定性, 具有良好的梯度流	收敛速度较慢, 对超参数敏感

应用阶段	参考文献	采用的模型	解决的问题	面临的挑战
观察	[27-30]	GAN, CGAN	自动特征提取，实现更准确的信号表征；端到端的信号处理流程，简化信号处理流程；高质量的噪声抑制，提高信号识别的准确性	训练过程的稳定性需要提高，生成信号的质量缺乏客观评价标准
判断	[31-59]	RCGAN, WGAN, ACGAN, LDCGAN, GAN, U-net GAN, CountGAN, AC-WGAN, InfoGAN	通过生成新的信号样本，缓解小样本学习问题；通过结合不同网络结构，提升信号源识别准确率；在缺乏标注数据的情况下利用无监督学习提高目标检测与分类的准确性；通过学习正常数据分布为异常检测提供新视角	训练过程中可能产生模式单一，降低样本多样性；需要大量计算资源，可能限制其在资源受限环境下的实用性
决策	[60-68]	DRGAN, WGAN-GP, CGAN, ACGAN, LSGAN, DCGAN, CGAN	利用强大的学习能力，生成与任意信号相似的波形模拟干扰攻击场景，增强对抗策略的适应性和决策制定能力	可能容易受到对抗性攻击，威胁系统安全性；在需要实时信号处理的决策中，GAN的推理速度需要进一步提升
行动	[69-72]	GAN, DJTGAN	利用对抗性学习提高欺骗成功率；扩展至多天线应用场景，增强复杂环境中的欺骗攻击效果；生成高度匹配的干扰模板，丰富干扰数据库内容	技术实现复杂度较高：GAN的训练和应用需深入理解原理和网络结构；实时处理需求高：在行动阶段，GAN的推理速度需满足快速响应的要求

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Research progress on generative adversarial network for electromagnetic information intelligent control

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