Classification of UAVsPresence, Type and Operation Mode Using Convolutional Neural Networks

Abstract

The omnipresence of unmanned aerial vehicles (UAVs) in the civilian air space has led to their malicious usage raising high alert security issues. The countermeasures for detection and prevention of such activities are highly required. This work proposes aconvolutional neural network (CNN) to identify the flight modes of the UAVsfrom theremotely sensed radio frequency (RF) signatures. AnRF receiver is deployed to intercept the communication between the UAV and itsflight controller. Since deploying deep learning has witnessed the remarkable performance in almost every field of engineering and sciences, the proposed model comprises a neural network to identify the UAVs presence, its type and its operation mode. Furthermore, the considered flight modes of UAVsare classified into four categories:turned on and connected to its controller, hovering, flying without video recording, and flying with video recording. Finally, the performance of CNN is evaluated using various parameters of confusion matrix which confirms the viability of the proposed system.