南京航空航天大学学报（英文版）2021，Vol.38Issue(4)：597-606,10.
一种基于射频信号倒频谱的民用无人机识别和分类方法
Detection and Classification on Amateur Drones Based on Cepstrum of Radio Frequency Signal
摘要
Abstract
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.关键词
无人机识别/射频信号/倒频谱/机器学习Key words
drone detection/radio frequency signal/cepstrum/machine learning分类
信息技术与安全科学引用本文复制引用
管祥民,马健翔,张维东..一种基于射频信号倒频谱的民用无人机识别和分类方法[J].南京航空航天大学学报（英文版）,2021,38(4):597-606,10.基金项目
This study was co-supported by the National Natural Science Foundation of China(Nos.U1933130,71731001,1433203,U1533119),and the Re-search Project of Chinese Academy of Sciences(No.ZDRW-KT-2020-21-2). （Nos.U1933130,71731001,1433203,U1533119）
