A unified framework for joint mobility prediction and object profiling of drones in UAV networks

Abstract

In recent years, using a network of autonomous and cooperative unmanned aerial vehicles (UAVs) without command and communication from the ground station has become more imperative, particularly in search-and-rescue operations, disaster management, and other applications where human intervention is limited. In such scenarios, UAVs can make more efficient decisions if they acquire more information about the mobility, sensing and actuation capabilities of their neighbor nodes. In this study, we develop an unsupervised online learning algorithm for joint mobility prediction and object profiling of UAVs, to facilitate control and communication protocols. The proposed method not only predicts the future locations of the surrounding flying objects, but also classifies them into different groups with similar levels of maneuverability (e.g., rotatory and fixed-wing UAVs) without prior knowledge regarding these classes. This method is flexible in admitting new object types with unknown mobility profiles, and is thereby applicable to emerging flying ad-hoc networks (FANETs) with heterogeneous nodes.