Radio Frequency Fingerprint Identification Technology Considering Strong Interference of Electromagnetic Noise

Abstract

To improve the anti-interference ability of RF fingerprint identification technology, the study adopts the improved Sketch algorithm to screen the raw data and label the data by growing self-organizing model. Next, a residual network model with channel attention mechanism is used for feature extraction and classification, and a customized nonlinear activation function and dynamic threshold noise reduction algorithm are introduced. The results indicated that the Sketch algorithm was able to effectively control the estimation error of high-frequency sub-signals within 14%. The differences in classification clusters of K-means and growing self-organizing model clustering algorithms were 0.2691 and 0.2639, with time overheads of 8.6 s and 2.3 s. The recognition accuracy of the residual network model based on the channel-attention mechanism was 98.2%, respectively, higher than that of the other three comparative models when the signal-to-noise ratio was 10 dB. It is shown that the performance performance and robustness of the model can be further improved by optimizing the shape and size of the network using the attention mechanism and adaptive methods. The research and application of this method is of great significance for improving the accuracy and robustness of RF signal fingerprinting.