电讯技术2026，Vol.66Issue(4)：629-636,8.DOI:10.20079/j.issn.1001-893x.250113005
结合特征分布优化的辐射源类增量识别方法
Incremental Identification of Radiation Source Classes Combined with Feature Distribution Optimization
摘要
Abstract
The existing models for class incremental recognition of radiation source are affected by catastrophic forgetting,resulting in low recognition rates and imbalance between stability and plasticity.A radiation source class incremental recognition method based on classifier incremental learning and feature distribution optimization is proposed.The new class features are transformed by Gaussian distribution to improve the distribution and increase the recognition rate of the new classes.For the increasing of the recognition rate of the old classes,feature distribution similar to the old classes is generated by utilizing a small amount of inter-and intra-cluster distribution information.The stability and plasticity are eventually balanced through Gaussian distribution and the inter-and intra-cluster distribution.The average accuracy of the proposed method on a mobile phone radiation source dataset and a radio radiation source dataset reaches 96.38%and 94.02%,respectively,which indicates that this method can effectively improve the recognition rate of radiation source while balancing its stability and plasticity.关键词
辐射源识别/类增量学习/灾难性遗忘/特征分布优化Key words
radiation source identification/class incremental learning/catastrophic forgetting/feature distribution optimization分类
信息技术与安全科学引用本文复制引用
彭鹏,曹帅,贾勇,姚光乐,王琛,王洪辉,张伟,王祥丰..结合特征分布优化的辐射源类增量识别方法[J].电讯技术,2026,66(4):629-636,8.基金项目
国家自然科学基金资助项目(U20B2070) （U20B2070）
四川省重点研发项目(2022YFS0531) （2022YFS0531）
