Target localization with a distributed Kalman filter over a network of UAVs
Salvatore Bassolillo, Egidio D’Amato, Massimiliano Mattei, Immacolata Notarodownload PDF
Abstract. Unmanned Aerial Vehicles (UAVs) have gained significant usage in various kinds of missions, including reconnaissance, search and rescue, and military operations. In rescue missions, timely detection of missing persons after avalanches is crucial for increasing the chances of saving lives. Using UAVs in such scenarios offers benefits such as reducing risks for rescuers and accelerating search efforts. Employing a formation of multiple drones can effectively cover a larger area and expedite the process. However, the challenge lies in achieving autonomous and scalable systems, as drones are typically operated on a one-to-one basis, requiring a large team of rescuers. To enhance situational awareness and distribute communication load, this paper proposes a decentralized Kalman filtering algorithm that exploits sensor data from multiple drones to estimate target positions and support guidance and control algorithms. The algorithm combines Consensus on Information and Consensus on Measurements techniques. Preliminary validation is conducted through numerical simulations in a sample scenario.
Distributed Kalman Filter, Target Localization, UAV Formation
Published online 11/1/2023, 4 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Salvatore Bassolillo, Egidio D’Amato, Massimiliano Mattei, Immacolata Notaro, Target localization with a distributed Kalman filter over a network of UAVs, Materials Research Proceedings, Vol. 37, pp 94-97, 2023
The article was published as article 21 of the book Aeronautics and Astronautics
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 F. Y. Hadaegh, S.-J. Chung, and H. M. Manohara, On Development of 100-Gram-Class Spacecraft for Swarm Applications, IEEE Systems Journal, vol. 10, no. 2, pp. 673–684, Jun. 2016. https://doi.org/10.1109/JSYST.2014.2327972
 D. Guha-Sapir, F. Vos, and R. Below, Annual Disaster Statistical Review 2011. 2012.
 L. Ruetten, P. A. Regis, D. Feil-Seifer, and S. Sengupta, Area-Optimized UAV Swarm Network for Search and Rescue Operations, in 2020 10th Annual Computing and Communication Workshop and Conference (CCWC), Jan. 2020, pp. 0613–0618. https://doi.org/10.1109/CCWC47524.2020.9031197
 M. Cicala, E. D’Amato, I. Notaro, and M. Mattei, Scalable Distributed State Estimation in UTM Context, Sensors, vol. 20, no. 9, Art. no. 9, Jan. 2020. https://doi.org/10.3390/s20092682
 M. A. Azam, S. Dey, H. D. Mittelmann, and S. Ragi, Average Consensus-Based Data Fusion in Networked Sensor Systems for Target Tracking, in 2020 10th Annual Computing and Communication Workshop and Conference (CCWC), Jan. 2020, pp. 0964–0969. https://doi.org/10.1109/CCWC47524.2020.9031250