Malicious UAV Detection over Rician Fading Channel: Performance Analysis

Unmanned aerial vehicles (UAVs) are predicted to be widely used in both military and civilian sectors in the coming years due to their high mobility, low cost, and enhancement of the line-of-sight (LoS) conditions in non-terrestrial networks. Nevertheless, this raises some security concerns if they are manipulated to cause security threats in restricted locations, or even cause privacy breaches. In order to detect malicious UAVs, radio frequency (RF)-based approaches are adopted to detect ambient RF signals, which can be accomplished with inexpensive RF sensors under both LoS and, in particular, non-line-of-sight (NLoS) conditions. In this paper, we propose a passive detection technique based on received signal strength (RSS), and derive analytical expressions on the detection and false alarm probabilities considering realistic air-to-ground (A2G) channel conditions. A novel low-complexity suboptimal detector is also proposed and its performance is compared to the optimal detection. Monte Carlo simulations are used to confirm the accuracy of the derived expressions under the aforementioned channel conditions. Our mathematical framework, analytical derivations, and simulation results reveal that the sensing node can achieve an accuracy of 0.9 under LoS scenarios, where the NLoS conditions cause some challenges in the accuracy of detection. The proposed low-complexity suboptimal detector for urban and suburban environments has close performance compared to the optimal detection.