电讯技术2026，Vol.66Issue(6)：943-950,8.DOI:10.20079/j.issn.1001-893x.250912001
基于多维特征的FPV无人机个体识别方法
FPV Drone Individual Recognition Based on Multi-dimensional Features
王子健 1李歆昊 1谷业伟1
作者信息
- 1. 国防科技大学 电子对抗学院,合肥 230037
- 折叠
摘要
Abstract
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%.关键词
FPV无人机/个体识别/多维特征/ResNet模型Key words
FPV drone/individual identification/multi-dimensional features/ResNet model分类
信息技术与安全科学引用本文复制引用
王子健,李歆昊,谷业伟..基于多维特征的FPV无人机个体识别方法[J].电讯技术,2026,66(6):943-950,8.
