Phase-coherent accumulation noise elimination-based radio frequency fingerprint feature extraction and recognition method

Abstract

The invention discloses a phase-coherent accumulation noise elimination-based radio frequency fingerprint feature extraction and recognition method. The method comprises a phase-coherent accumulation noise elimination step S4 which is used for a radio frequency fingerprint identification technology and a step S6 which is used for performing multi-resolution analysis on waveforms in the radio frequency fingerprint identification technology. According to the S4, phase-coherent accumulation is performed on a plurality of power-on instantaneous signal sample point amplitude value functions of the same device, so that signal strength can be multiple times of original signal strength, and phase-coherent accumulation noise elimination signals are obtained. According to the S6, multi-resolution analysis is performed on the phase-coherent accumulation noise elimination signals obtained in the step A. According to the phase-coherent accumulation noise elimination-based radio frequency fingerprint feature extraction and recognition method of the invention, the phase-coherent accumulation method is adopted in the radio frequency fingerprint identification technology; the signal-to-noise ratio of the waveforms is improved, so that the accuracy of radio frequency identification can be improved; and on the basis of improving the accuracy of the radio frequency identification based on the phase-coherent accumulation, the multi-resolution analysis is utilized, and therefore, the computational complexity of the support vector machine-based radio frequency fingerprint identification technology is reduced.

Description

technical field [0001] The invention relates to the field of physical layer access authentication of wireless devices, in particular to a radio frequency fingerprint feature extraction and identification method based on coherent accumulation and denoising. Background technique [0002] Feature extraction comes from image processing. In signal detection and processing, it is a method of analyzing and transforming the preprocessed signal to highlight the representative (with obvious physical or statistical significance) features of the signal and Extract saved method. The purpose of feature extraction is to reduce data storage and reduce input data redundancy, so as to facilitate the discovery of more meaningful latent variables, and help in-depth understanding and analysis of a large number of similar signals. RF fingerprint feature extraction is a key step in RF fingerprint identification. For the received wireless signal, the receiver must preprocess the signal, such as so...

AI Technical Summary

Problems solved by technology

In addition, although the use of support vector machines can improve the accuracy of radio frequency fingerprint recognition, the demand for the number of sample points for support vector machine learning is very large, resulting in high computational complexity and high computational resource overhead.

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