Drone Detection Approach Based on Radio-Frequency Using Convolutional Neural Network

Recently, Unmanned Aerial Vehicles, also known as drones, 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 day to day applications such as cinematography, agriculture monitoring and search and rescue, they are also being used in malicious activities that are targeting to harm individuals and societies which raises great privacy, safety and security concerns. In this research, we propose a new drone detection solution based on the Radio Frequency (RF) emitted during the live communication session between the drone and its controller using a Deep Learning (DL) technique, namely, 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 RF based drone detection using Deep Neural Networks.