中圖分類號：TN911.7
文獻標識碼：A
DOI:10.19358/j.issn.2097-1788.2023.05.012
引用格式：劉高輝，于文濤.基于深度自適應小波網(wǎng)絡的通信輻射源個體識別［J］.網(wǎng)絡安全與數(shù)據(jù)治理，2023，42（5）：71-77.

Individual recognition of communication radiation source based on depth adaptive wavelet network

Abstract： Aiming at the problem of the complex artificial features extracted in the existing individual recognition methods of communication radiation sources and the lack of clear interpretation of the recognition mechanism of deep learning networks, an individual recognition method of communication radiation sources based on Deep Adaptive Wavelet Network (DAWN) is proposed. Firstly, the intermodulation interference is analyzed as the reason for individual characteristics between radiation sources. Then, the convolutional neural network structure that can realize lifting wavelet transform is applied to extract features, based on which DAWN can complete feature extraction and recognition at the same time. Finally, Oracle data sets are selected to verify the feasibility of the method. The experimental results show that the accuracy of identification of 5 communication radiation sources by DAWN is 955%, and the method has good antinoise performance.

Key words : specific emitter identification；lifting wavelet transform；depth adaptive wavelet network