  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, in particular 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 paper, 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 about these classes. This method is flexible in admitting new object
types with unknown mobility profiles, thereby applicable to emerging flying
Ad-hoc networks with heterogeneous nodes.
