Collaborative spectrum sensing for illegal drone detection: A deep learning-based image classification perspective

Abstract

Drones, also known as mini-unmanned aerial vehicles (UAVs), are enjoying great popularity in recent years due to their advantages of low cost, easy to pilot and small size, which also makes them hard to detect. They can provide real time situational awareness information by live videos or high definition pictures and pose serious threats to public security. In this article, we combine collaborative spectrum sensing with deep learning to effectively detect potential illegal drones with states of high uncertainty. First, we formulate the detection of potential illegal drones under illegitimate access and rogue power emission as a quaternary hypothesis test problem. Then, we propose an algorithm of image classification based on convolutional neural network which converts the cooperative spectrum sensing data at a sensing slot into one image. Furthermore, to exploit more information and improve the detection performance, we develop a trajectory classification algorithm which converts the flight process of the drones in consecutive multiple sensing slots into trajectory images. In addition, simulations are provided to verify the proposed methods' performance under various parameter configurations.