东南大学学报（英文版）2017，Vol.33Issue(4)：432-436,5.DOI:10.3969/j.issn.1003-7985.2017.04.007
基于全局-局部特征提取算法的信号分类系统
Signal classification system using global-local feature extraction algorithm
摘要
Abstract
A continuous wavelet transform (CWT) and globallocal feature (GLF) extraction-based signal classification algorithm is proposed to improve the signal classification accuracy.First,the CWT is utilized to generate the timefrequency scalogram.Then,the GLF extraction method is proposed to extract features from the time-frequency scalogram.Finally,a classification method based on the support vector machine (SVM) is proposed to classify the extracted features.Experimental results show that the extended binary phase shift keying (EBPSK) bit error rate (BER) of the proposed classification algorithm is 1.3 × 10-5 under the environment of additional white Gaussian noise with the signal-to-noise ratio of-3 dB,which is 24 times lower than that of the SVM-based signal classification method.Meanwhile,the BER using the GLF extraction method is 13 times lower than the one using the global feature extraction method and 24 times lower than the one using the local feature extraction method.关键词
连续小波变换/支持向量机/全局-局部特征/信号分类Key words
continuous wavelet transform (CWT)/support vector machine (SVM)/global-local features/signal classification分类
信息技术与安全科学引用本文复制引用
方兰婷,吴乐南,张煜东..基于全局-局部特征提取算法的信号分类系统[J].东南大学学报（英文版）,2017,33(4):432-436,5.基金项目
The National Key Technology R&D Program (No.2012BAH15B00),the Scientific Innovation Research of College Graduates in Jiangsu Province (No.KYLX150076). （No.2012BAH15B00）
