Radio Frequency Fingerprint Identification Technology Considering Strong Interference of Electromagnetic Noise

To improve the anti-interference ability of RF fingerprint identification technology, the study adopts the improved sketch algorithm to screen the raw data. The extraction of important features and high-frequency signals from raw data is achieved through a series of steps, including partitioning, calculation of local correlation, data filtering, and aggregation. This process is facilitated by the use of a growing self-organizing model, which labels the data. 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. This model employs a two-dimensional convolutional kernel to safeguard the phase feature information of the I/Q data, and integrates a channel attention mechanism and a dynamic threshold function. The results demonstrated that the sketch algorithm was capable of effectively controlling the estimation error of high-frequency sub-signals to 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 residual network model based on the channel-attention mechanism exhibited a recognition accuracy of 98.2%, which was 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. © 2024 Slovene Society Informatika. All rights reserved.