A Radio Frequency Fingerprint Identification Method for Wireless Devices Based on Multi-Feature Fusion

Radio frequency fingerprint identification can be used for device authentication, network access management, malicious device tracking and identity spoofing attacks. Current research on Radio Frequency Fingerprint Identification (RFFI) mostly relies on single signal features, using deep learning algorithms for feature representation and pattern recognition to improve identification capabilities, but there is a problem of low recognition accuracy. Aiming at the problem of wireless device RF fingerprint identification, this article presents a wireless device RF fingerprint recognition method based on multi-feature fusion. This method fuses three signal features with high time-frequency resolution: Short Time Fractional Fourier Transfer (STFRFT), Down Sampling of Short Time Fourier Transfer (DS-STFT) and Smoothed Pseudo Wigner-Ville Distribution (SPWVD) as fingerprints. These are then fed into a deep residual network (ResNet34) for device classification. Simulation experiments show that a series of experiments based on Bluetooth device datasets have verified that the proposed method is superior to single-feature methods in terms of accuracy and robustness. At the same time, even under conditions where a certain signal-to-noise ratio causes signal fluctuations to overwhelm device features, this method can still effectively classify transmitting devices.