电讯技术2026，Vol.66Issue(4)：622-628,7.DOI:10.20079/j.issn.1001-893x.241019001
基于元学习驱动的辐射源个体识别
Meta-learning-driven Individual Identification of Radiation Sources
摘要
Abstract
For the problem of feature distribution differences between the training data and the data of real application scenarios in the task of radiation source individual identification,a novel meta-learning-based method for radiation source individual identification is proposed.The method is able to improve the generalization ability of the feature extractor by learning common features across different tasks,and achieve coarse-grained feature alignment with fewer samples in the target domain and without the need to re-train the model.The method aims to extend the diversity of target domain data through an effective data enhancement strategy to train and obtain initialization parameters with strong generalization in the framework of meta-learning.On this basis,a fine-tuning strategy is introduced to achieve more accurate feature alignment and classification results,which significantly enhances its generalization performance in new scenarios.Experimental results show that the method does not need to retrain the model and can achieve 96%transfer accuracy on LoRa and WiSig datasets.In addition,the method effectively reduces the computation and storage requirements by virtue of its lightweight design and can be deployed in distributed nodes.关键词
辐射源个体识别/闭集识别/迁移学习/元学习Key words
radiation source individual identification/closed set identification/transfer learning/meta-learning分类
信息技术与安全科学引用本文复制引用
门浩轩,吴昊,乔晓强,杜奕航,张涛,张江..基于元学习驱动的辐射源个体识别[J].电讯技术,2026,66(4):622-628,7.基金项目
国家自然科学基金资助项目(62371463) （62371463）
