海军航空大学学报2024，Vol.39Issue(5)：523-534,12.DOI:10.7682/j.issn.2097-1427.2024.05.002
数据恶劣条件下的辐射源个体识别方法综述
Overview of Individual Identification Methods for Radiation Sources Under Harsh Data Conditions
摘要
Abstract
Individual identification methods for radiation sources under harsh data conditions are analyzed and compared.Individual recognition methods including imbalance,mislabeling,small samples,and weak labeling are summarized,the advantages and limitations of radiation source feature extraction methods are explored,the key and difficult feature extrac-tion methods in the methods are summarized,and the advantages of deep learning in deep feature extraction and its broad application prospects in the field of radiation source individual recognition are pointed out,with a view providing a com-prehensive supplement to individual identification methods for radiation sources in various situations.关键词
辐射源个体识别/不平衡识别/小样本识别/错误标签/弱标注/深度学习Key words
individual identification for radiation sources/imbalance identification/small sample identification/mislabe-ing/weak labeling/deep learning分类
信息技术与安全科学引用本文复制引用
闫文君,段可欣,凌青,李春雷,黄丽..数据恶劣条件下的辐射源个体识别方法综述[J].海军航空大学学报,2024,39(5):523-534,12.基金项目
国家自然科学基金面上项目(62371465) （62371465）
电磁空间安全全国重点实验室开放基金 （）
