An RF Fingerprint Extraction Method based on Time-frequency Domain Feature Fusion. Issue 1 (1st January 2023)

• Record Type: Journal Article
• Authors: Jin, Yanhua; Wei, Mengfan; Li, Qiuxue
• Is Part Of: Journal of physics. Volume 2424:Issue 1(2023)
• Publication Date: 2023-01-01
• Subjects: RF fingerprint -- Feature Extraction -- WVD -- Time Domain Statistics; Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2424/1/012030
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: Identification and authentication of communication radiation sources using RF fingerprint feature extraction technology is widely used in civilian and military applications and has become a research hotspot. In this paper, an RF fingerprint extraction method based on time-frequency domain feature fusion is proposed. The frequency domain features of WVD and the time domain statistical features (variance, skewness, kurtosis) of the signal are first extracted, and then fused and downscaled using multivariate discriminant analysis to obtain new time-frequency fusion features, which are finally input to the support vector machine for classification and recognition. This method is used to analyze signals of walkie-talkies and signals of cell phones with and without noise, and the experimental results show that this method effectively improves the recognition rate and has good noise immunity compared with using frequency domain features or time domain features alone, and the accuracy is improved by about 5% under the condition of low SNR. When the SNR is -10dB, the recognition accuracy of walkie-talkies can reach 81.6% and that of cell phones can reach 86.1%.