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作 者:方章闻 张金艺[1,2] 李科[2] 姜玉稀 FANG Zhangwen;ZHANG Jinyi;LI Ke;JIANG Yuxi(Microelectronic Research and Development Center, Shanghai University, Shanghai 200444, China;Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200444, China;Shanghai Sansi Institute for System Integration, Shanghai 201100, China)
机构地区:[1]上海大学微电子研究与开发中心,上海200444 [2]上海大学特种光纤与光接入网重点实验室,上海200444 [3]上海三思系统集成研究所,上海201100
出 处:《系统工程与电子技术》2020年第10期2381-2389,共9页Systems Engineering and Electronics
基 金:十三五国家重点研发计划(2017YFB0403500);上海市教委重点学科资助项目(J50104)资助课题。
摘 要:在通信辐射源信号有标签样本数量较小的情况下,同类通信辐射源个体信号特征提取困难且识别精度较低。对此,提出了一种小样本条件下的通信辐射源半监督特征提取方法。该方法对少量有标签通信辐射源信号样本以及大量无标签通信辐射源信号样本进行变分模态分解提取高维稳态信息熵,利用指数半监督判别分析法映射信息熵形成个体特征,并通过XGBoost进行通信辐射源个体识别来验证识别效果。实验表明,所提方法识别准确率达到85.33%,相比无监督特征提取方法运算时间降低了76.17%,证明其在同类通信辐射源不同个体识别中具有较好的性能。In the case of few label samples of the communication emitter signal,it is difficult to extract individual features of similar emitter signals and the identification accuracy is low.To this regard,a semi-supervised feature extraction method of communication emitter under the small sample condition is proposed.A small number of labeled emitter signal samples and a large number of unlabeled emitter signal samples are subjected by variational mode decomposition to extract high-dimensional steady-state information entropy.The exponential semi-supervised discriminant analysis is used to map the information entropy to form individual features.In addition,XGBoost is used to identify the communication emitter to verify the identification effect.Experiments show that the proposed method reduces the computation time by 76.17%compared with the unsupervised feature extraction method,and the identification rate reaches 85.33%,which proves that it has better performance in different individual identification of similar communication emitters.
关 键 词:通信辐射源个体识别 特征提取 变分模态分解 指数半监督判别分析
分 类 号:TN911.7[电子电信—通信与信息系统]
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