计算机技术与发展2026，Vol.36Issue(1)：73-79,7.DOI:10.20165/j.cnki.ISSN1673-629X.2025.0219
基于OVA与伪标签优化的辐射源域适应识别方法
Radiation Source Domain Adaptation Recognition Method Based on OVA and Pseudo-label Optimization
摘要
Abstract
We propose a domain adaptation-based method for radiation source individual recognition,aiming to address the issue of low recognition accuracy of traditional methods under different domain samples.The method introduces a One-Vs-All(OVA)classifier,training a binary classifier for each category.By minimizing the entropy between the positive class and its nearest negative class,the positive class space is compressed to enhance prediction reliability.In addition,a consistency evaluation and sample balancing strategy is designed by combining the outputs of the closed-set classifier and the OVA classifier.These strategies are used to filter high-quality pseudo-labels and optimize the model adaptation for target domain samples.Experiments are conducted on the Oracle and Wisig datasets,which show that the proposed method outperforms existing methods under various noise conditions.Under-3 dB noise conditions,the accuracy on the Oracle dataset reaches 69.61%,and on the Wisig dataset,it reaches73.55%,representing improvements of approximately 1～8 percentage points and 5～18 percentage points compared to other methods,respectively.Under multiple noise con-ditions,the average accuracy on the Oracle dataset is87.36%,and on the Wisig dataset,it is89.11%,which are about1～3 percentage points higher than that of the comparison methods.The proposed method demonstrates stronger robustness and adaptability in high-noise environments,providing an effective solution for radiation source individual recognition.关键词
辐射源个体识别/域适应/一对多网络/伪标签生成/样本均衡筛选Key words
specific emitter identification/domain adaptation/One-Vs-All network/pseudo-label generation/sample balancing screening分类
信息技术与安全科学引用本文复制引用
王闯,俞璐,潘成,李宁,杜文博..基于OVA与伪标签优化的辐射源域适应识别方法[J].计算机技术与发展,2026,36(1):73-79,7.基金项目
国家自然基金项目(62471486) （62471486）
