Edge UAV Detection Based on Cyclic Spectral Feature: An Intelligent Scheme

Abstract

With the commercialization of the fifth-generation mobile communication network (5G), the scale of the unmanned aerial vehicle (UAV) industry has continued to expand. However, the unregistered UAV has caused frequent harassment incidents at international airports, and the problem of UAV crimes is increasing. Radio technology supports long-distance detection of unregistered UAV and can be used as an efficient early warning method for unregistered UAV, which has attracted extensive attention from academia and industry. The classic UAV detection based on remote control signal method faces technical bottlenecks such as being easily affected by environmental noise, high complexity, and low detection accuracy. In the paper, an UAV remote control signal detection method is proposed based on cyclic spectrum features. More specifically, a dataset of UAV remote control signal UAV-CYCset is firstly constructed in the frequency domain. Based on UAV-CYCset dataset, a network architecture is proposed based on improved AlexNet, and the average detection accuracy of the improved model reaches 85% (from -10 dB to 10 dB) according to the simulation experiments.