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Differential constellation trace figure (DCTF) has been demonstrated good performance on radio frequency fingerprint (RFF) identification. However, DCTF easily blurs at low SNRs. This paper proposes two novel RFF identification methods for ZigBee devices based on multi-intervals DCTFs. First, a low-complexity motion features extraction method is devised based on high-density regions. Besides, an improved 3D-2D CNN model is utilized to extract motion features and spatial features. We collected 54 different ZigBee devices for experiments and classified them by these two methods. The experimental results show that compared with using single DCTF, which is generated by a single differential interval, these two methods can effectively improve the identification accuracy at different SNR levels. The classification accuracy rate of the 3D-2D CNN classifier is over 92% even under the SNR level of 5 dB.

Radio Frequency Fingerprint Identification Based on Multi-Intervals Differential Constellation Trace Figures