Frequency hopping radio individual identification based on time frequency spectrum characteristics

For the individual identification problem of frequency hopping radio stations, a feature extraction algorithm based on time-frequency energy spectrum was proposed. Firstly, using the sparseness of frequency hopping signals in the time-frequency domain, the time-frequency energy spectrum was obtained by a sparse reconstruction method. Then, the time-frequency energy spectrum was segmented under different scale conditions, and the three characteristics of time-frequency distribution Rayleigh entropy, multi-fractal dimension and differential box dimension were extracted. Finally, the support vector machine classifier was used to train, classify and identify the extracted feature set to realize the individual identification of the frequency hopping station. Using the frequency hopping signals of four stations, the recognition performance of the proposed algorithm and the other two algorithms were compared. The experimental results show that the subtle feature set extracted by this method has strong resolving power, avoids the misjudgment caused by the similarity of single features, and can maintain a high recognition accuracy under a small number of training samples.