WiFi device radio frequency fingerprint identification method based on small sample image classification

By adopting small sample image classification method and data enhancement technology in the WiFi device recognition system, the problem of low accuracy of RF fingerprint recognition under small sample conditions is solved, and high-precision recognition under limited samples is achieved.

CN119691726AActive Publication Date: 2025-03-25SICHUAN UNIV +1
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Patent Information

Application Number
CN202411775637.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-05
Publication Date
2025-03-25
Estimated Expiration
2044-12-05

AI Technical Summary

Technical Problem

Under small sample conditions, the accuracy of radio frequency fingerprint recognition of WiFi devices is low, making it difficult to effectively identify and manage WiFi devices.

Method used

A small sample image classification method is used to obtain WiFi device signal data for preprocessing, and convert it into time spectrum diagrams using short-time Fourier transform to construct a small sample data set. Then, a radio frequency fingerprint small sample image classification model is constructed based on the twin neural network, and an image data augmentation module is designed to improve recognition accuracy through data augmentation and model training.

Benefits of technology

The accuracy of RF fingerprint recognition of WiFi devices is significantly improved under small sample conditions, and can achieve higher classification accuracy with fewer training samples.

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Abstract

The invention relates to a WiFi device radio frequency fingerprint identification method based on small sample image classification. The method comprises the following steps: acquiring WiFi equipment signal data, preprocessing the WiFi equipment signal data, converting the preprocessed WiFi equipment signal data into a time-frequency spectrogram by adopting short-time Fourier transform, and constructing a small sample data set; constructing a radio frequency fingerprint small sample image classification model based on a twin neural network, and designing an image data enhancement module based on an auto-encoder; pre-training the radio frequency fingerprint small sample image classification model and the image data enhancement module by adopting a small sample data set; sample expansion is carried out based on the image data enhancement module, and screening is carried out by using a pre-trained radio frequency fingerprint small sample image classification model; and training the radio frequency fingerprint small sample image classification model again by using the expanded data set, inputting a test sample after training is completed, and outputting an identification result. And the accuracy of radio frequency fingerprint identification of the WiFi equipment under the small sample condition is improved.
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