To address the issues of high computational complexity and poor robustness in traditional methods based on hand-crafted feature extraction,this paper proposes a radio frequency fingerprint recognition method that combines an attention mecha-nism with a Convolutional Neural Network(CNN).This method leverages the powerful feature extraction capabilities of the model,while the attention mechanism focuses on key information.Without manual intervention,it reduces complexity while improving recog-nition efficiency and accuracy in interference-prone scenarios.Experimental results on radio frequency signal datasets from different scenarios and signal types show that,compared to CNN without the attention mechanism,the proposed method significantly improves recognition accuracy for 5 and 10 types of USRP,reaching 99.5%and 98.1%,respectively.This validates the method's effectiveness and advantage in individual emitter recognition,without the need for preprocessing or manually designed fingerprint features.
关键词
射频指纹识别/卷积神经网络/注意力机制/辐射源
Key words
radio frequency fingerprint identification/convolutional neural network/attention mechanism/radiation source
万方数据