小样本雷达辐射源识别的深度学习方法综述

Survey of Deep Learning for Radar Emitter Identification Based on Small Sample

面对复杂多变的电磁环境与新体制雷达系统,传统的雷达辐射源识别方法已无法满足需求。深度学习模型可有效提取雷达信号的脉内特征,快速准确地对低信噪比、未经分选的雷达辐射源信号进行脉内调制类型识别、型号识别与个体识别。但真实环境中雷达辐射源信号难以收集,无法满足传统的深度学习训练需要,因此小样本雷达辐射源识别是目前研究的热点与难点。文中首先对近年来将基于监督学习的多种经典深度学习方法应用于小样本雷达辐射源识别的研究进行了回顾;其次,介绍了小样本学习在雷达辐射源识别领域的研究进展;最后,基于小样本雷达辐射源识别的研究现状,总结面临的挑战,提出了对未来研究方向的展望。

Traditional radar emitter identification methods can no longer meet the needs of identifying new-system radar emitters in the complicate and changeable electromagnetic environment.Deep learning methods can effectively extract the intra-pulse features of the unsorting radar emitter signal,quickly and accurately identify the radar intra-pulse modulation type,model type and emitter individual under complex environments such as low signal-to-noise ratio.However,in the reality,radar emitter signal is difficult to collect and cannot satisfy the training needs of traditional deep learning models.Therefore,the small sample radar emitter identification is one of hotspot and difficult questions of current research.Firstly,this paper reviews the research progress and application of various deep learning methods based on supervised learning for radar emitter recognition with small samples in recent years.Secondly,the research progress of radar emitter identification by small sample learning is introduced.Last,according to the current radar emitter identification research,the challenges and outlook for future research are put forward.