数据采集与处理2018，Vol.33Issue(1)：22-31,10.DOI:10.16337/j.1004-9037.2018.01.003
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
摘要
Abstract
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 regular-ized 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 learn-ing.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Key words
communication transmitter/fine feature/bispectrum/locally neighborhood preserving regular-ization/semi-supervised learning分类
信息技术与安全科学引用本文复制引用
雷迎科..复杂电磁环境下通信辐射源个体细微特征提取方法[J].数据采集与处理,2018,33(1):22-31,10.基金项目
国防科技重点实验室基金(9140C130502140C13068)资助项目 （9140C130502140C13068）
总装预研项目基金(9140A33030114JB39470)资助项目 （9140A33030114JB39470）
国家自然科学基金(61272333,61473237)资助项目 （61272333,61473237）
安徽省自然科学基金(1308085QF99,1408085MF129)资助项目. （1308085QF99,1408085MF129）
