太赫兹科学与电子信息学报2024，Vol.22Issue(2)：142-151,159,11.DOI:10.11805/TKYDA2023181
基于动态权重模型的数据不平衡SEI方法
Data imbalance SEI method based on dynamic weight model
摘要
Abstract
To tackle with the problem of decreased recognition accuracy caused by imbalanced individual data distribution in Specific Emitter Identification(SEI),a dynamic weight model based method is proposed for individual identification of radiation sources.A Dynamic Class Weight(DCW)model is built.A moderate initial weight value is obtained by using a meta learning algorithm through two-layer calculation with a small amount of sample data.Then,a new cost sensitive loss function is designed to calculate the backward adjustment of the distance between the predicted value and the true value,which gives the minority learning weight,and moderately increases the attention to the minority data.It is more friendly to the minority.It has obvious advantages in the processing of highly unbalanced data,which alleviates the calculation misleading of the majority of samples in the whole recognition process,thus improving the overall recognition accuracy.关键词
辐射源个体识别/不平衡数据/动态类权重/元学习/代价敏感损失Key words
Specific Emitter Identification/unbalanced data/Dynamic Class Weights/meta learning/cost sensitive losses分类
信息技术与安全科学引用本文复制引用
段可欣,闫文君,刘凯,张建廷,李春雷,王艺卉..基于动态权重模型的数据不平衡SEI方法[J].太赫兹科学与电子信息学报,2024,22(2):142-151,159,11.基金项目
国家自然科学基金面上资助项目(62271499 （）
62371465) （）
电磁空间安全全国重点实验室开放基金资助项目 （）
