Abstract: Recently, Unmanned Aerial Vehicles (UAV)’s, also known as drone, are becoming rapidly popular due to the advancement of their technology and the significant decrease in their cost. Although, commercial drones have proven their effectiveness in many applications such as cinematography, agriculture monitoring, search and rescue, building 3D inspection, and many other day to day applications, they have also been alarmingly used in malicious activities that are targeting to harm individuals and societies which raises great privacy, safety and security concerns. Hence, in this research, we propose a new solution drone detection solution based on the radio frequency emitted during the live communication session between the drone and its controller using the convolutional neural network (CNN). The results of the study have proven the effectiveness of using CNN for drone detection with accuracy and F1 score of over 99.7% and drone identification with accuracy and F1 score of 88.4%. Moreover, the results yielded from this experiment have outperformed those reported in the literature for Radio Frequency (RF) based drone detection using Deep Neural Networks.

Authors: Sara A. Al-Emadi, Felwa AlSenaid

Conference: Submitted to 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) – IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT’20)

Accepted on 19th December 2019

You can find the full text on IEEE or an early draft here

The code can be found on Github


Cite this paper via the following BibTeX:

@INPROCEEDINGS{ AlEm2003:Drone, AUTHOR=”Sara A Al-Emadi and Felwa Al-Senaid”, TITLE=”Drone Detection Approach Based on {Radio-Frequency} Using Convolutional Neural Network”, BOOKTITLE=”2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) (ICIoT’2020)”, ADDRESS=”, Qatar”, DAYS=14, MONTH=mar, YEAR=2020 }

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