Individual identification of electronic equipment based on electromagnetic fingerprint characteristics

With the rapid development of communication and computer, the individual identification technology of communication equipment has been brought to many application scenarios. The identification of the same type of electronic equipment is of considerable significance, whether it is the identification of friend or foe in military applications, identity determination, radio spectrum management in civil applications, equipment fault diagnosis, and so on. Because of the limited-expression ability of the traditional electromagnetic signal representation methods in the face of complex signals, a new method of individual identification of the same equipment of communication equipment based on deep learning is proposed. The contents of this paper include the following aspects: (1) Considering the shortcomings of deep learning in processing small sample data, this paper provides a universal and robust feature template for signal data. This paper constructs a relatively complete signal template library from multiple perspectives, such as time domain and transform domain features, combined with high-order statistical analysis. Based on the inspiration of the image texture feature, characteristics of amplitude histogram of signal and the signal amplitude co-occurrence matrix (SACM) are proposed in this paper. These signal features can be used as a signal fingerprint template for individual identification. (2) Considering the limitation of the recognition rate of a single classifier, using the integrated classifier has achieved better generalization ability. The final average accuracy of 5 NRF24LE1 modules is up to 98% and solved the problem of individual identification of the same equipment of communication equipment under the condition of the small sample, low signal-to-noise ratio.