The Research Of Radio Signal Classification Methods Based On Wavelet Neural Network

With the development of communication technology, wireless communication has become more and more complicated. Various modulation types are used in the communication signals with broad bandwidth. How to monitor and identify these signals is an essential subject of communication signal processing, and an important part in electronic countermeasures too. At the same time, signal identification is a rapidly evolving area of signal analysis. The automatic identification of signal modulations has been applied many fields,such as identification, interference identification, radio interception and monitoring, satellite communications, etc.Wavelet transformation has a good localization characteristic in time-frequency domain, while the neural network has characteristics of self-study, self-adaptation, high stabilization and error acceptability. Using neural network can improve the automatization and intelligence of recognition. Based on above, wavelet analysis and neural network are used to implement the classification of the modulation signals. AM,DSB,USB,LSB,FM,PM,2ASK,4ASK,2FSK,4FSK,2PSK,4PSK are researched in this thesis.The theory of signal modulation is introduced first, and these modulation signals are realized with MATLAB. Then a method of classification based on wavelet neural network is designed in this thesis. In the step of characteristic extraction, wavelet transformation is used to analyze the twelve modulation signals and extract the characteristic parameters. In the step of classification, the characteristics of signals which are extracted in samples are used to train the RBF neural network. RBF export the classification result when the error meets the requirement. Finally the method is simulated with MATALB. Under a 5dB of signal to noise ratio, the average recognition ratio is 98.58%, and the lowest recognition ratio is 97%. The simulation results indicate that the presented method performs well.