电子科技大学学报2025，Vol.54Issue(4)：501-506,6.DOI:10.12178/1001-0548.2024111
基于无监督特征提取的辐射源识别
Specific emitter identification based on unsupervised feature extraction
摘要
Abstract
Imperfections in the analog components of emitters cause distortions in the transmitted signals.These distortions serve as unique fingerprints for the purpose of specific emitter identification(SEI).Features for SEI are typically extracted using either a distortion-models based method or a machine learning based method.In this study,these two methods for feature extraction from emitters are investigated.Incorporating specialist knowledge,the distortion model,into the neural network,a cascade network mode is proposed to extract the parameters in-phase/quadrature imbalance and phase noise models of the emitter,which not only ensures the interpretability of the extracted features and enhance identification accuracy.Simulation results demonstrate that this scheme outperforms both the conventional distortion-models-based and the machine learning-based methods in terms of feature extraction performance.关键词
无监督特征提取/特定发射器识别/同相-正交不平衡/相位噪声Key words
unsupervised feature extraction/specific emitter identification(SEI)/in-phase/quadrature imbalance/phase noise分类
信息技术与安全科学引用本文复制引用
王颖舒,张娟娟,袁舒,任未知,熊文汇,雷霞..基于无监督特征提取的辐射源识别[J].电子科技大学学报,2025,54(4):501-506,6.基金项目
国家级基金项目(G022023KP01602) （G022023KP01602）
南方电网科技创新基金项目(GZKJXM20220026) （GZKJXM20220026）
