Self-Learning Bayesian Generative Models for Jammer Detection in Cognitive-UAV-Radios

Unmanned Aerial Vehicles (UAVs) attracted both industry and research community owing to their fascinating features like mobility, deployment flexibility and strong Line of Sight (LoS) links. The integration of Cognitive Radio (CR) can greatly help UAVs to overcome several issues especially spectrum scarcity. However, the dynamic radio environment in CR and the strong dependence of safe communications from LoS channels integrity in UAV communications make the Cognitive-UAV-Radio vulnerable to jamming attacks. This work aims to study the integration of CR and UAVs introducing a Self-Awareness (SA) framework from the physical layer security perspective. Under the SA framework, a Dynamic Bayesian Network (DBN) model is proposed as a representation of the radio environment and a modified Markov Jump Particle Filter (MJPF) is employed for prediction and state estimation purposes. A novel jammer detection framework is proposed that allows the UAV to perform abnormality evaluation at different hierarchical levels. The jammer is shown to be located effectively in both time and frequency domains. Experimental results show the effectiveness of the proposed framework in terms of detection probability and accuracy.