Abstract:

Radio frequency fingerprinting is a technology that uses the uniqueness of the radio frequency characteristics of the radiation source to identify its individual identity, which has broad application prospects for military and civilian applications. Aiming at the problem that the traditional RF fingerprint features were not stable and difficult to implement, a radio frequency fingerprint identification method based on multi-dimensional frequency domain characteristics such as radiation source frequency offset and phase noise was proposed. Taking the ADS-B signal as an example, statistical principles were utilized to quantify frequency offset and phase noise characteristics, and the Bayesian classification method was applied to classify the signals to finally realize the identification of the individual identity. The algorithm was validated by semi-physical generation and real-time ADS-B signal data. The experimental results showed that the method could effectively distinguish the same type of radiation source, and the recognition rate of the four targets in the real environment was 93.5%.

Key words: radio frequency fingerprinting, frequency offset, phase noise, carrier frequency stability, Bayesian classification, ADS-B