Radiation source individual identification using machine learning method

Due to the small sample of communication station signals and the weak fingerprint characteristics of radio stations, the accuracy of individual identification of communication stations is not high. This paper firstly proposes the identification of communication stations based on machine learning method, which can be used without training samples. Firstly, the signal samples are subjected to rectangular integral bispectral transformation, and the 1×L-dimensional rectangular bispectrum features are extracted. Then these features of sample signals will be sent to clustering model and finally these signals will be classified into different groups. Compared with the traditional c classification method, this method uses the clustering in machine learning method, which is an unsupervised way. It does not need samples signal from a communication station with a label, and it will play a greater role in practical application.