Detection Performance of Malicious UAV using Massive IoT Networks

This paper investigates the fundamental performance limits in detecting malicious drones and mini-unmanned aerial vehicles (UAVs) using massive RF-based sensors under multipath fading channels. Although drones and/or small UAVs have many civilian and military applications, their prevalence raised security concerns if they have been controlled to breach into restricted areas. In this work, the RF-based sensing of unauthorized drones is adopted with well-distributed sensors in an urban environment. Detection performance using Neyman-Pearson criterion with Bayesian inference is analyzed and closed-form expressions for the probability of detection are derived. The derived expressions are corroborated with extensive Monte-Carlo simulations to demonstrate the severe effect of environmental conditions, e.g. suburban/dense, observation dimensions of the sufficient statistics, and sensor locations on the detection performance.