基于改进卷积神经网络和射频指纹的无人机检测与识别  

UAV detection and recognition based on improved convolutional neural network and radio frequency fingerprint

作　　者：周景贤[1] 李希娜 ZHOU Jingxian;LI Xina(Information Security Evaluation Center,Civil Aviation University of China,Tianjin 300300,China;School of Computer Science and Technology,Civil Aviation University of China,Tianjin 300300,China)

机构地区：[1]中国民航大学信息安全测评中心,天津300300 [2]中国民航大学计算机科学与技术学院,天津300300

出　　处：《计算机应用》2024年第3期876-882,共7页journal of Computer Applications

基　　金：中央高校基础研究基金资助项目(3122018C036);民航安全能力项目(PESA219074,PESA021009)。

摘　　要：针对无人机(UAV)在图像识别时易受环境干扰,而传统信号识别难以准确提取特征且实时性较差的问题,提出一种基于改进卷积神经网络(CNN)和射频(RF)指纹的无人机检测识别方法。首先,使用通用软件无线电外设(USRP)捕获环境中的无线电信号,经过多分辨率分析获取偏差值,检测是否为无人机射频信号;其次,将检测到的无人机射频信号经过小波变换和主成分分析(PCA)处理,获得射频信号频谱,作为神经网络的输入;最后,构建轻量级残差神经网络(LRCNN),输入射频频谱进行网络训练,进行无人机的分类识别。实验结果表明,所提方法可以有效检测并识别无人机信号,平均识别精度可达84%;在信噪比(SNR)大于20 dB时,LRCNN的识别精度达到了88%,相较于支持向量机(SVM)、原始OracleCNN分别提高31和7个百分点,在识别精度和鲁棒性方面比这两种方法均有所提升。In order to solve the problems that the UAV(Unmanned Aerial Vehicle)is vulnerable to environmental interference in image recognition,and the traditional signal recognition is difficult to accurately extract features and has poor real-time performance,a UAV detection and recognition method based on improved CNN(Convolutional Neural Network)and RF(Radio Frequency)fingerprint was proposed.Firstly,a USRP(Universal Software Radio Peripheral)was used for capturing radio signals in an environment,a deviation value was obtained through multi-resolution analysis,to detect whether the radio signal was an unmanned aerial vehicle radio frequency signal or not.Secondly,the detected unmanned aerial vehicle radio frequency signal was subjected to wavelet transformation and PCA(Principal Component Analysis)to obtain a radio frequency signal spectrum which was used as an input of a neural network.Finally,a LRCNN(Lightweight Residual Convolutional Neural Network)was constructed,and the RF spectrum was input to train the network for UAV classification and recognition.Experimental results show that LRCNN can effectively detect and recognize UAV signals,and the average recognition accuracy reaches 84%.When the SNR(Signal-to-Noise Ratio)is greater than 20 dB,the recognition accuracy of LRCNN reaches 88%,which is 31 and 7 percentage points higher than those of SVM(Support Vector Machine)and the original OracleCNN,respectively.Compared with these two methods,LRCNN has improved recognition accuracy and robustness.

关 键 词：无人机安全 射频指纹 小波变换 注意力残差网络 卷积神经网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]