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

TL;DR

This paper introduces an unsupervised online learning framework that predicts UAV mobility and profiles their capabilities, enhancing decision-making in autonomous UAV networks for critical applications.

Contribution

It presents a novel joint prediction and profiling algorithm that operates without prior class knowledge, adaptable to heterogeneous and emerging UAV networks.

Findings

Accurately predicts future UAV locations.

Classifies UAVs into mobility-based groups.

Adapts to new, unknown UAV types.

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, 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.