基于时频和双谱特征融合的DA-ResNeXt50射频指纹识别方法  

DA-ResNeXt50 method for radio frequency fingerprint identification based on time-frequency and bispectral feature fusion

作　　者：陈梦迪 张巍 沈雷[2] 雷富强 张佳飞 CHEN Mengdi;ZHANG Wei;SHEN Lei;LEI Fuqiang;ZHANG Jiafei(School of Mechanical Engineering,Hangzhou Dianzi University,Hangzhou 310018,China;School of Communication Engineering,Hangzhou Dianzi University,Hangzhou 310018,China)

机构地区：[1]杭州电子科技大学机械工程学院,浙江杭州310018 [2]杭州电子科技大学通信工程学院,浙江杭州310018

出　　处：《电信科学》2024年第9期54-65,共12页Telecommunications Science

摘　　要：针对射频指纹识别中单一特征无法全面表示信号的完整性,且类间特征差异较小从而限制识别准确率等问题,提出了一种基于时频和双谱特征融合的DA-ResNeXt50(ResNeXt50 with dense connection and ACBlock)射频指纹识别方法。首先,对采集到的不同设备的信号分别进行短时傅里叶变换(short-time Fourier transform,STFT)和双谱变换,将得到的图像二值化处理并拼接,综合利用两种变换分别在时频域和高阶统计特性上的优势,更全面地提取和表征不同设备的射频指纹特征;然后,提出了DA-ResNeXt50网络模型,借鉴密集连接思想,使四层残差单元每一层都与前面所有层直接相连,促进了特征的复用和传递,能更好地捕捉类间细微差异;最后,使用非对称卷积模块(asymmetric convolution block,ACBlock)替换模型最后一层残差单元的3×3卷积,可以有效地增加网络的感受野,增强卷积核的骨架部分,从而提高射频指纹识别性能。实验结果表明,相较于使用单一特征提取方法,提出的特征融合方法的性能有较大的提升,改进后的模型与多种经典模型相比,具有较高的识别精度。To address the problems that a single feature in radio frequency fingerprint recognition could not fully represent the integrity of the signal and that the differences between features of different classes were small,which limited the recognition accuracy,a DA-ResNeXt50(ResNeXt50 with dense connection and ACBlock)method for radio frequency fingerprint identification based on time-frequency and bi-spectral feature fusion was proposed.Firstly,short-time Fourier transform(STFT)and bi-spectrum transform were performed separately on the signals collected from different devices,the resulting images were bi-narized and then concatenated.By taking advantage of the advantages of both transformations in the time-frequency domain and high-order statistical characteristics respectively,the radio frequency fingerprint features of different devices can be extracted and characterized more comprehensively.Then,the DA-ResNeXt50 network model was proposed.Borrowing from the idea of dense connection,each layer of the four-layer residual unit was directly connected to all previous layers,promoting feature reuse and transmission,which enabled it to better capture subtle differences between classes.Finally,the asymmetric convolution block(ACBlock)was used to replace the 3×3 convolution in the last residual unit of the model.This effectively increased the receptive field of the network and enhanced the skeleton part of the convolutional kernel,thereby improving the performance of radio frequency fingerprint recognition.The experimental results show that compared with that of using a single feature extraction method,the proposed feature fusion approach significantly improves performance.Compared with various classical models,the improved model has higher recognition accuracy.

关 键 词：短时傅里叶变换 双谱变换 射频指纹 密集连接 非对称卷积 

分 类 号：TN911.7[电子电信—通信与信息系统]