太赫兹科学与电子信息学报Issue(2)：260-265,6.
基于高阶累积量的核Logistic回归调制分类算法
A method of modulation classification of Kernel Logistic Regression based on high-order cumulants
徐闻 1王斌1
作者信息
- 1. 解放军信息工程大学 信息工程学院，河南 郑州 450002
- 折叠
摘要
Abstract
Aiming to the problem of automatic modulation classification of the existing digital signal, a classification method based on Kernel Logistic Regression(KLR) is developed.This method is primarily used in economic,medical science and speech process etc,while seldom applied in the field of communication signals. The characteristic parameter of high-order cumulants of the signal is used for training data and testing data.The classification is performed adopting the frequently-used decision tree method. The proposed method is compared to the modulation classification method based on Support Vector Machine(SVM) through simulation experiments. The results indicate that the proposed method is qualified to do the work.Under low SNR(0 dB),the performance of classification is higher than that based on SVM;while under 5dB,the correct recognition rate is above 90%based on KLR.关键词
调制识别/分类/高阶累积量/核 Logistic回归/决策树Key words
modulation recognition/classification/high-order cumulants/Kernel Logistic Regression/decision tree分类
信息技术与安全科学引用本文复制引用
徐闻,王斌..基于高阶累积量的核Logistic回归调制分类算法[J].太赫兹科学与电子信息学报,2013,(2):260-265,6.
