指挥控制与仿真2026，Vol.48Issue(1)：60-65,6.DOI:10.3969/j.issn.1673-3819.2026.01.008
基于多通道扩张密集卷积网络的电磁信号识别
Electromagnetic signal recognition model based on multi-channel dilated dense convolutional networks
兰嵩 1刘彬 2赵雅琦 2郭安业 2张学斌 2孔维侧2
作者信息
- 1. 武警福建省总队,福建 福州 350001||国防科技大学电子对抗学院,安徽 合肥 230026
- 2. 武警福建省总队,福建 福州 350001
- 折叠
摘要
Abstract
This paper addresses the issue of low accuracy in electromagnetic signal recognition when applying existing deep learning networks.It studies classic deep learning network solutions for electromagnetic signal recognition both domestically and internationally,comparing and analyzing the strengths and weaknesses of various approaches.Subsequently,it proposes an electromagnetic signal classification method based on multi-channel feature extraction and dilated convolutional neural net-works.By extracting the signal graph features,spectrum graph features,and double-layer CNN learned features input into a dilated dense convolutional network model for classification and recognition.By constructing and training a model on the RA-DAR dataset,the experiment achieved a high recognition rate.Additionally,through ablation experiments,the importance and effectiveness of each component of the model were validated.Finally,this paper discusses the limitations of the model in complex electromagnetic environments and the directions for future improvement.关键词
电磁空间/电磁信号识别/扩张卷积神经网络/多通道特征提取Key words
electromagnetic space/electromagnetic signal recognition/dense convolutional neural networks/multi-channel feature extraction分类
信息技术与安全科学引用本文复制引用
兰嵩,刘彬,赵雅琦,郭安业,张学斌,孔维侧..基于多通道扩张密集卷积网络的电磁信号识别[J].指挥控制与仿真,2026,48(1):60-65,6.
