Convolutional Neural Networks for Drone Model Classification

被引：0

作者：

Dale, H. ^{[1
]}

Antoniou, M. ^{[1
]}

Baker, C. J. ^{[1
]}

Jahangir, M. ^{[1
]}

Catherall, A. ^{[2
]}

机构：

[1] Univ Birmingham, Microwave Integrated Syst Lab, Birmingham, W Midlands, England

[2] Plextek, London Rd, Saffron Walden, Essex, England

来源：

2021 18TH EUROPEAN RADAR CONFERENCE (EURAD) | 2021年

基金：

英国工程与自然科学研究理事会;

关键词：

staring radar; radar applications; convolutional neural networks; deep learning; UAVs;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This work establishes performances for drone group (fixed wing vs rotary wing), drone subgroup (fixed wing vs hexacopter vs quadcopter) and drone model classification using a convolutional neural network (CNN). Data is from an experimental campaign with nine different drone models flying along various trajectories. It is demonstrated that CNNs are very capable of drone recognition, with baseline performances as high as 98%.

引用

页码：361 / 364

页数：4

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