Open AccessFrequency hopping radio individual identification based on energy spectrum blended subtle characteristics
Author(s) -
Xin Yang,
Qi Zhang,
Shaobo Wang
Publication year - 2019
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1325/1/012221
Subject(s) - frequency hopping spread spectrum , feature extraction , fractal , pattern recognition (psychology) , fractal dimension , computer science , energy (signal processing) , entropy (arrow of time) , artificial intelligence , frequency band , classifier (uml) , radio spectrum , spectral density , support vector machine , mathematics , algorithm , bandwidth (computing) , statistics , telecommunications , physics , mathematical analysis , quantum mechanics
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.