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In recent years, using a network of autonomous and cooperative unmannedaerial vehicles (UAVs) without command and communication from the groundstation has become more imperative, in particular in search-and-rescueoperations, disaster management, and other applications where humanintervention is limited. In such scenarios, UAVs can make more efficientdecisions if they acquire more information about the mobility, sensing andactuation capabilities of their neighbor nodes. In this paper, we develop anunsupervised online learning algorithm for joint mobility prediction and objectprofiling of UAVs to facilitate control and communication protocols. Theproposed method not only predicts the future locations of the surroundingflying objects, but also classifies them into different groups with similarlevels of maneuverability (e.g. rotatory, and fixed-wing UAVs) without priorknowledge about these classes. This method is flexible in admitting new objecttypes with unknown mobility profiles, thereby applicable to emerging flyingAd-hoc networks with heterogeneous nodes.

A Unified Framework for Joint Mobility Prediction and Object Profiling of Drones in UAV Networks