复杂电磁环境下通信辐射源个体细微特征提取方法  

Novel Fine Feature Extraction Method for Identifying Communication Transmitter in Complex Electromagnetic Environment

作　　者：雷迎科[1,2] Lei Yingke(Electronic Engineering Institute,Hefei,230037,China;Science and Technology on Communication Information Security Control Laboratory,Jiaxing,314033,China)

机构地区：[1]电子工程学院,合肥230037 [2]通信信息控制和安全技术重点实验室,嘉兴314033

出　　处：《数据采集与处理》2018年第1期22-31,共10页Journal of Data Acquisition and Processing

基　　金：国防科技重点实验室基金(9140C130502140C13068)资助项目;总装预研项目基金(9140A33030114JB39470)资助项目;国家自然科学基金(61272333;61473237)资助项目;安徽省自然科学基金(1308085QF99;1408085MF129)资助项目

摘　　要：针对实际复杂电磁环境下通信辐射源个体细微特征提取面临的标签样本缺失问题,将半监督学习理论引入到通信辐射源细微特征提取,提出一种半监督框架下的局部近邻保持正则化判别分析方法。该方法在双谱估计的基础上,通过向线性判别模型中有效融入由无标签样本所提供的流形结构信息,从而将线性判别方法扩展到半监督学习。在实际采集的同种型号、同种厂家、相同批次以及相同工作模式的不同FM通信电台数据集上的实验结果表明,该方法能够获得更优的分类识别性能。To cope with the problem that the traditional fine feature extraction methods for identifying communication transmitters suffer from the lack of the labeled samples in real complex electromagnetic environment,an efficient fine feature extraction method,called locally neighborhood preserving regularized semi supervised discriminant analysis,is proposed for communication transmitter recognition.Based on the bispectrum estimation,manifold structure information is incorporated into the linear discriminant model by unlabeled samples,which extends the linear discriminant analysis to the semi supervised learning.Extensive experiments on the real world database sampled from different FM communication radios with the same model,manufacturer,manufacturing lot,and work pattern demonstrate that the proposed method can obtain better recognition performance.

关 键 词：通信辐射源 细微特征 双谱 局部近邻保持正则化 半监督学习 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]