无线电工程2024，Vol.54Issue(6)：1337-1345,9.DOI:10.3969/j.issn.1003-3106.2024.06.001
基于深度学习的通信辐射源识别综述
A Review of Communication Specific Emitter Identification Based on Deep Learning
王育欣 1马宏斌 1马宏 1焦义文 1李雪健 1侯顺虎1
作者信息
- 1. 航天工程大学智能化航天测运控教育部重点实验室,北京 101416
- 折叠
摘要
Abstract
Under non-cooperative conditions,signal detection,automatic modulation mode identification and Specific Emitter Identification(SEI)are crucial in battlefield communication reconnaissance.With the rapid development of wireless communication technology,the types of radiation sources have become increasingly diverse,the signal system has become more complex,and the harsh electromagnetic environment has brought significant challenges to SEI.In recent years,with the rapid advancement of deep learning and its practical applications in fields such as natural language processing and computer vision,it has gradually been applied in SEI tasks and has achieved rich research results.Given the lack of open-source datasets in the existing literature,available open-source datasets are compiled and a detailed review of SEI methods is conducted from two dimensions:knowledge-driven and data-driven approaches,including expert system methodologies and deep learning technologies.The comparative analysis reveals the advantages of deep learning in SEI tasks.Finally,the development directions of SEI are summarized,concerning the existing problems faced by deep learning in the field of SEI.关键词
通信辐射源/辐射源个体识别/深度学习/数据驱动/开集识别Key words
communication radiation source/SEI/deep learning/data-driven/open-set identification分类
信息技术与安全科学引用本文复制引用
王育欣,马宏斌,马宏,焦义文,李雪健,侯顺虎..基于深度学习的通信辐射源识别综述[J].无线电工程,2024,54(6):1337-1345,9.
