Communication station individual identification based on density peak algorithm

 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 density peak algorithm, 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 the Euclidean distance between each signal is calculated, and the density ρ and δ of each signal are calculated according to the definition of the density peak algorithm.With ρ and δ as A two-dimensional map is drawn on the abscissa and the ordinate, and the cluster center is found, and each signal is classified and identified. Compared with the traditional communication station classification and recognition method, this method uses the clustering method in machine learning, 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.