摘要
针对现有数字信号调制识别的问题,提出了一种基于核Logistic回归(KLR)的自动分类方法。该方法提取了信号的高阶累积量参数用作训练与测试数据,采取常用的决策树分类构架的思想,仿真并比较已有的基于支撑向量机(SVM)的调制分类方法,结果表明,在低信噪比为0 dB时,分类性能一般高于SVM;5 dB时,采用KLR的分类识别率均达到90%以上,有较为优越的分类性能。
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（O 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.
出处
《太赫兹科学与电子信息学报》
2013年第2期260-265,共6页
Journal of Terahertz Science and Electronic Information Technology
关键词
调制识别
分类
高阶累积量
核Logistic回归
决策树
modulation recognition
classification
high-order cumulants
Kernel Logistic Regression
decision tree
