An Optimized Radio Frequency Fingerprint Extraction Method Applied to Low-End Receivers

Abstract

This paper focuses on the method for classification and identification of radio transmitters in low-end receivers, to provide a new radio frequency fingerprint. The proposed algorithm based on the Empirical Mode Decomposition (EMD) is an optimized method for the actual radio frequency signal decomposition which will cause the problem of mode aliasing. Firstly, a signal superposition denoising process is applied before EMD to reduce the influence of channel noise. And then, an autocorrelation function discrimination method is performed to distinguish different modal components after EMD. Thirdly, box dimension is used to extract features to form feature vectors as the radio frequency fingerprint which is classified by the neural network classifier, defining the transmitting device of the signal. The experiment results show that the algorithm has a good recognition effect in the low-end signal receiving equipment USRP, and it can effectively recognize the signals from different interphone individuals, which verifies the effectiveness of the algorithm.