Frequency hopping radio individual identification based on energy spectrum blended subtle characteristics

Abstract

For the problem of low recognition rate and large computational complexity of frequency hopping radio stations, a blended subtle characteristic extraction method based on energy spectrum is proposed. Considering that the time-frequency energy distribution is widely used in individual feature extraction, the time-frequency energy spectrum of the frequency hopping signal is obtained by the sparse reconstruction method firstly. Then the fractal theory is used to extract the feature vector set including time-frequency energy spectrum Rayleigh entropy, multi-fractal dimension and difference box dimension with multi-scale block extraction. Finally, the support vector machine classifier is used to train, classify and identify the feature set to realize the individual identification of the frequency hopping station. The recognition performance of the proposed method and the other two methods are compared and verified through the frequency hopping signals of four stations. The experimental results show that compared with the other two methods, the proposed method has higher recognition rate under low SNR and a small number of training samples.