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| In the background of artificial intelligence (AI) being widely applied, radio frequency (RF) fingerprint identification employing AI has become a trend. This paper mainly focuses on ways to process and identify RF signal transmitters. Contrapose to traditional RF fingerprint feature extracting and identifying methods, a method for modeling and extracting the RF signals features using integral bispectrum firstly was raised. Afterwards, A convolutional neural network has been proposed for classification of the RF individual transmitters. For verification, several mobile phones (with same or different type/brand) signals were processed by the above methods. The experiments results show that the proposed methods make up the weaknesses in accuracy and efficiency of previous used GBDT, XGBoost, and stacking algorithms. Moreover, the signal model derived by the proposed methods can describe individual signal transmitter with same type and brand, which is superior to signal model mentioned in references and has great significance in individual RF fingerprint identification. |
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| Keywords:signal processing, bispectral feature, convolutional neural networks, ensemble learning, individual identification,RF Fingerprint |
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