Signal Classification Implemented by Wavelet Analysis and Support Vector Machine

In this thesis, a method for signal classification has been developed and implemented on the platform of Matlab and Libsvm, which combines the techniques of wavelet analysis and support vector machine. The whole process is divided into three stages i.e. Signal Generation, Feature Extraction and Classification. There are 6 types of modulated signals i.e. BPSK, QPSK, FSK, ASK, 4ASK and QAM generated and decomposed by Biorthogonal wavelet to obtain the detail components from each signal. The energy level of each detail components are calculated and forms a feature vector representing the identification of the signal itself prepared to be classified in the vector space of SVM classifier. The classification results shows that the performance of classification works well if the signal to noise ratio (SNR) above 13dB in the range from 1dB to 30dB, which indicates it is feasible working under a certain noise level to classify those defined 6 types of modulated signals. Furthermore, we analyze the reasons that cause the different performance of the signals on the classification test and also discuss the limitation and the possible development of the method in the end.