RF-Based UAV Surveillance System: A Sequential Convolution Neural Networks Approach

Abstract

In recent years, popularity of commercial unmanned air vehicles (UAVs) or drones enormously increased due to their ductility and availability in various applications domains. This also results in some security threats to sensitive area, that urgently needs proper investigation and surveillance system to protect the security sensitive institutions. In this paper, we propose a drone detection system which can detect drones and identify different types of drone respectively. The proposed network structure is constituted based on sequential convolution neural network (CNN) with several one-dimensional layer to successively learn the different scales feature map of radio frequency signals, collected from drone. To train the proposed CNN model, we use challenging DroneRF dataset, a free accessible database containing background noise and three different drone's radio frequency signals. The empirical results verify that the proposed model can detects all UAVs correctly and outperforms the existing RF based CNN model with average classification rate of 92.5% along with 93.5% F1 score.