一种去信道指纹的IEEE802.11a信号辐射源识别方法  

An IEEE802.11a signal radiation source identification method with channel fingerprint removal

作　　者：曾浩南 谢跃雷[1] Zeng Haonan;Xie Yuelei(School of Information and Communication,Guilin University of Electronic Technology,Guilin 541004,China)

机构地区：[1]桂林电子科技大学信息与通信学院,桂林541004

出　　处：《电子测量技术》2023年第17期125-130,共6页Electronic Measurement Technology

基　　金：广西科技重大专项(桂科AA21077008)资助。

摘　　要：针对现有射频指纹识别技术中,使用卷积神经网络提取射频指纹容易受到信道指纹干扰,而导致识别精度急剧下降的问题,提出了一种去信道指纹的IEEE802.11a信号辐射源识别方法。首先提取出待识别信号帧头的时域训练序列,然后利用标准IEEE802.11a时域训练序列作为参考信号,结合LMS自适应滤波器对待识别信号进行信道均衡与补偿;最后采用IQCNet模型从时域信号中提取射频指纹特征进行设备身份识别。实验结果表明,在不同的无线信道环境下,对6台基于IEEE802.11a协议的无线路由器的识别正确率最高达到了96%,能有效去除信道指纹对射频指纹识别带来的不良影响。Aiming at the problem that the radio frequency fingerprint(RFF)extracted by convolutional neural network(CNN)is easily interfered by the channel fingerprint,resulting in a sharp decrease in recognition accuracy.An IEEE802.11a signal radiation source identification method with channel fingerprint removal was proposed.Firstly,extract the time-domain training sequence of the frame head of the signal to be recognized,and the time-domain training sequence is used as the reference signal.Then use the LMS adaptive filter and time-domain training sequence for channel equalization and compensation.Finally,IQCNet model is used to extract the RFF from the time domain signal for device identification.The experimental results show that the recognition rate of 6 wireless routers based on IEEE802.11a protocol reaches up to 96%in different wireless channel environments.The proposed method can effectively remove the negative influence of channel fingerprint on RFF identification.

关 键 词：射频指纹识别 深度学习 信道均衡 LMS自适应滤波 

分 类 号：TN911.7[电子电信—通信与信息系统] TP183[电子电信—信息与通信工程]