雷达科学与技术2024，Vol.22Issue(1)：104-110,118,8.DOI:10.3969/j.issn.1672-2337.2024.01.014
基于数据增强的小样本辐射源个体识别方法
Few-Shot Sample Specific Emitter Identification Method Based on Data Augmentation
摘要
Abstract
Aiming at the dilemma of low recognition accuracy of few-shot learning and due to difficult acquisition of sample data and incomplete capture sample categories,a method for few-shot specific emitter identification(SEI)based on data enhancement is proposed.Firstly,the dataset is expanded by time domain flipping,amplitude inversion,amplitude scaling and noise processing.Secondly,the noise sequence and the category label are input into the generator to further generate the"false and true"generated samples,which improves the diversity of the generated samples and synchronously completes discrimination and category prediction of true and false samples through the auxiliary classifi-er.Finally,according to the dynamic feedback of the discriminator,the weight of the loss function is gradually adjusted,and the network is further optimized by focusing on high-quality samples to improve the recognition accuracy.关键词
辐射源个体识别/小样本/数据增强/辅助分类生成对抗网络Key words
specific emitter identification(SEI)/few-shot samples/data augmentation/auxiliary classifier GAN分类
信息技术与安全科学引用本文复制引用
王艺卉,闫文君,段可欣,于楷泽..基于数据增强的小样本辐射源个体识别方法[J].雷达科学与技术,2024,22(1):104-110,118,8.基金项目
国家自然科学基金面上项目(No.62271499,62371465) （No.62271499,62371465）
电磁空间安全全国重点实验室开放基金 （）
