利用循环平稳性检测和支持向量机的调制信号分类  

Modulation Classification Using Cyclostationarity Test and Support Vector Machine

作　　者：吴量[1] 江桦[1] 崔伟亮[1,2] 

机构地区：[1]信息工程大学信息工程学院 [2]68002部队科技装备处

出　　处：《应用科学学报》2013年第6期593-600,共8页Journal of Applied Sciences

基　　金："十一.五"国家重点项目基金(No.2009AA011504)资助

摘　　要：非合作接收条件下调制识别算法预处理要求高,分类集有限,为此提出一种基于循环频率特征和支持向量机的调制分类方法.利用信号循环累积量的循环频率为分类特征,不必进行参数估计和同步等预处理,在缺乏先验知识条件下对FSK、PSK、QAM、OQAM、CPOFDM、ZPOFDM等常见调制信号具有良好的识别效果.该方法提升了盲接收环境下的分类效果,扩大了分类集.理论推导和计算机仿真表明,该方法能有效提升非合作接收条件下的盲分类性能.Modulation recognition under non-cooperative reception conditions generally requires sophisticated preprocessing and has limited classification set. In the present paper, a modulation classification scheme based on cyclic frequency features and support vector machine classifier is proposed to improve the classification performance and expand the recognition set under blind reception conditions. The signal＇s cyclic frequency features of the cyclic cumulants are used to discriminate digital modulation signals including FSK, PSK, QAM, OQAM, CPOFDM, ZPOFDM, etc. The proposed method alleviates the preprocessing needs such as estimation of parameters and synchronization. Theoretical derivation is presented and simulations performed, showing effectiveness of the method.

关 键 词：调制分类 非合作接收 循环频率特征 支持向量机分类器 

分 类 号：TN911.7[电子电信—通信与信息系统]