Signal classification system using global-local feature extraction algorithm

A continuous wavelet transform (CWT) and global-local feature (GLF) extraction-based signal classification algorithm is proposed to improve the signal classification accuracy. First, the CWT is utilized to generate the time-frequency 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.