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

arXiv: https://arxiv.org/abs/1808.00058 · HTML: https://mlsanity.com/paper/1808.00058/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Jul 2018.

## Built on by (most cited first, 4 of 4)

- [A comprehensive survey of research towards AI-enabled unmanned aerial systems in pre-, active-, and post-wildfire management](https://mlsanity.com/paper/2401.02456/) (2024)
- [Distributed Machine Learning for UAV Swarms: Computing, Sensing, and Semantics](https://mlsanity.com/paper/2301.00912/) (2023)
- [Event-Only Drone Trajectory Forecasting with RPM-Modulated Kalman Filtering](https://mlsanity.com/paper/2603.01997/) (2026)
- [A Scalable Decentralized Reinforcement Learning Framework for UAV Target Localization Using Recurrent PPO](https://mlsanity.com/paper/2412.06231/) (2024)

## Abstract (arXiv)

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.