基于多维特征的FPV无人机个体识别方法OA

For the problems of low efficiency and large amount of computation in unmanned aerial vehicle(UAV)individual recognition,a first person view(FPV)drone individual recognition method based on multidimensional features is proposed.This method constructs a two-layer architecture of"rapid screening of external features-deep analysis of signal multidimensional features".First,based on the external feature extraction and threshold judgment of signals,rapid detection of analog image signals is realized and suspected signals are screened out.Then,the residual network(ResNet)model is used to perform fine-grained identification and matching of the screened suspect signals,so as to improve the accuracy and reliability of identification.The experimental results show that the rejection rate of the fast screening layer of the proposed method is more than 85%,and the average recognition accuracy of the deep parsing layer for FPV drone signals in the 5.8 GHz band is 94%.