Radio frequency fingerprint identification method and system based on signal pilot frequency

Abstract

The invention relates to a radio frequency fingerprint identification method and system based on signal pilot frequency. The identification method comprises the following steps: acquiring an input signal; demodulating the input signal; positioning the initial position of the pilot frequency in the demodulated input signal by adopting a sliding window function method; determining a signal pilot frequency part according to the initial position of the pilot frequency to obtain a pilot frequency signal; calculating the power spectrum density of the pilot signal; and classifying the power spectrum density. The method is good in anti-noise performance and high in precision.

Description

technical field [0001] The invention relates to the field of wireless communication, in particular to a radio frequency fingerprint identification method and system based on signal pilots. Background technique [0002] With the rapid development of the Internet of Things technology, the era of Internet of Everything has arrived, and wireless communication technology has become an indispensable part of our lives. However, because wireless communication technology is more open than wired communication technology, it is also more vulnerable to attacks and threats, and network security issues have become increasingly prominent. The traditional way of preventing network attacks is usually based on the layer above the physical layer in the OSI network model, and the security of the wireless network is ensured by formulating keys, but the key-based authentication always has certain loopholes. Therefore, it is urgent to find a more secure way to ensure the security of wireless comm...

AI Technical Summary

Problems solved by technology

In 2008, Suski et al. proposed to use the variance trajectory method to perform transient detection based on amplitude and phase. After estimating the starting point of the transient, select the following pilot signal to draw the power spectral density (PSD), and then from the power spectral density The radio frequency fingerprints are extracted for classification. The correct estimation of the transient starting point has a great influence on the classification results. Finally, the classification and recognition of the three devices is only 80% correct under 6dB.

However, the existing RF fingerprinting technologies based on signal pilots cannot extract the pilot part of the signal accurately and efficiently. The method based on window function and power up can only extract one pilot at a time for continuous signals, and the method based on transient starting point estimation The method is greatly affected by channel noise, and cannot accurately find the starting position of the pilot, and the method based on human eye recognition is inefficient

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