一种结合双阈值与WSN的OFDM辐射源个体识别  

An OFDM radiation source individual identification combining dual thresholding and WSN

作　　者：刘高辉[1] 郑文文 Liu Gaohui;Zheng Wenwen(School of Automation and Information Engineering,Xi′an University of Technology,Xi′an 710048,China)

机构地区：[1]西安理工大学自动化与信息工程学院,陕西西安710048

出　　处：《网络安全与数据治理》2025年第3期39-46,共8页CYBER SECURITY AND DATA GOVERNANCE

基　　金：国家自然科学基金(61671375)。

摘　　要：针对在低信噪比情况下OFDM辐射源识别率低的问题,提出一种双阈值与小波散射网络(Wavelet Scattering Network,WSN)结合的OFDM信号时频图辐射源个体识别方法。首先,建立包含指纹特征的OFDM信号指纹模型;其次,利用小波变换对一个符号周期内的OFDM信号进行时频分析得到时频图;再次,设计一种双参数阈值函数模型实现自适应抑制噪声干扰,提高时频图的图像质量。经WSN处理后,从优化后的时频图中提取小波散射系数作为特征集;最后,采用ResNet18进行分类识别。仿真实验结果表明,该方法能够显著抑制噪声干扰,在信噪比为-4 dB的条件下,识别精度达到87.5%,相较于其他方法表现出更高的识别精度和抗噪性能。Aiming at the problem of low recognition rate of OFDM radiation source under low signal-to-noise ratio,a dual-threshold and wavelet scattering network(WSN)-based method is proposed for individual recognition of the radiation source in the time-frequency diagram of OFDM signal.Firstly,according to the working principle of OFDM transmitter,the fingerprint model of OFDM signal containing fingerprint features is established;secondly,the time-frequency diagram is obtained by time-frequency analysis of OFDM signal in one symbol period using wavelet transform.Again,a two-parameter threshold function model is designed to realize adaptive suppression of noise interference and improve the image quality of the time-frequency diagram.After WSN processing,wavelet scattering coefficients are extracted from the optimized time-frequency diagram as the feature set;finally,ResNet18 is used for classification and identification.The simulation results show that the method can significantly suppress noise interference,with recognition accuracy reaching 87.5%at a signal-to-noise ratio of-4 dB,outperforming other methods in terms of recognition accuracy and noise-resistant performance.

关 键 词：时频图 双阈值函数 小波散射网络 OFDM辐射源个体识别 

分 类 号：TN911[电子电信—通信与信息系统]