Detection and Classification on Amateur Drones Based on Cepstrum of Radio Frequency Signal  

一种基于射频信号倒频谱的民用无人机识别和分类方法

作　　者：GUAN Xiangmin MA Jianxiang ZHANG Weidong 管祥民;马健翔;张维东(中国民航管理干部学院民航通用航空运行重点实验室,北京100102;浙江建德通用航空研究院浙江省通用航空运行技术研究重点实验室,建德311612;北京航空航天大学成都创新研究院,成都611930;北京航空航天大学电子信息工程学院,北京100191)

机构地区：[1]Key Laboratory of General Aviation Operation,Civil Aviation Management Institute of China,Beijing 100102,P.R.China [2]Zhejiang Key Laboratory of General Aviation Operation Technology,General Aviation Institute of Zhejiang Jiande,Jiande 311612,P.R.China [3]Innovation Institute(Chengdu),Beihang University,Chengdu 611930,P.R.China [4]School of Electronic and Information Engineering,Beihang University,Beijing 100191,P.R.China

出　　处：《Transactions of Nanjing University of Aeronautics and Astronautics》2021年第4期597-606,共10页南京航空航天大学学报（英文版）

基　　金：co-supported by the National Natural Science Foundation of China (Nos. U1933130,71731001,1433203,U1533119);the Research Project of Chinese Academy of Sciences (No. ZDRW-KT-2020-21-2)。

摘　　要：As a prospective component of the future air transportation system,unmanned aerial vehicles(UAVs)have attracted enormous interest in both academia and industry.However,small UAVs are barely supervised in the current situation.Crash accidents or illegal airspace invading caused by these small drones affect public security negatively.To solve this security problem,we use the back-propagation neural network(BPNN),the support-vector machine(SVM),and the k-nearest neighbors(KNN)method to detect and classify the non-cooperative drones at the edge of the flight restriction zone based on the cepstrum of the radio frequency(RF)signal of the drone’s downlink.The signal from five various amateur drones and ambient wireless devices are sampled in an electromagnetic clean environment.The detection and classification algorithm based on the cepstrum properties is conducted.Results of the outdoor experiments suggest the proposed workflow and methods are sufficient to detect non-cooperative drones with an average accuracy of around 90%.The mainstream downlink protocols of amateur drones can be classified effectively as well.由于监管不利,由小型无人机引起的扰航及空域非法入侵等事故对公共安全造成了不良影响。为了解决此问题,本文使用反向传播神经网络算法、支持向量机算法和K近邻算法对位于禁飞区边缘的无人机的下行信号倒频谱进行识别和分类。在户外实地实验中收集了电磁静默环境下5种不同民用无人机的下行信号,并对这些信号进行了倒频谱分析。结果显示,本文提出的工作流和实现方法在非合作无人机的识别和分类方面取得了较好的效果,尤其在无人机识别方面,3种机器学习算法的平均准确率均可提升至近90%。

关 键 词：drone detection radio frequency signal CEPSTRUM machine learning 

分 类 号：TN925[电子电信—通信与信息系统]