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DroneScale: drone load estimation via remote passive RF sensing

Nguyen, Phuc, Kakaraparthi, Vimal, Bui, Nam, Umamahesh, Nikshep, Pham, Nhat, Truong, Hoang, Guddeti, Yeswanth, Bharadia, Dinesh, Han, Richard, Frew, Eric, Massey, Daniel and Vu, Tam 2020. DroneScale: drone load estimation via remote passive RF sensing. Presented at: SenSys '20: The 18th ACM Conference on Embedded Networked Sensor Systems, Virtual Event Japan, 16-19 November 2020. SenSys '20: Proceedings of the 18th Conference on Embedded Networked Sensor Systems. Association for Computing Machinery, pp. 326-339. 10.1145/3384419.3430778

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Drones have carried weapons, drugs, explosives and illegal packages in the recent past, raising strong concerns from public authorities. While existing drone monitoring systems only focus on detecting drone presence, localizing or fingerprinting the drone, there is a lack of a solution for estimating the additional load carried by a drone. In this paper, we present a novel passive RF system, namely DroneScale, to monitor the wireless signals transmitted by commercial drones and then confirm their models and loads. Our key technical contribution is a proposed technique to passively capture vibration at high resolution (i.e., 1Hz vibration) from afar, which was not possible before. We prototype DroneScale using COTS RF components and illustrate that it can monitor the body vibration of a drone at the targeted resolution. In addition, we develop learning algorithms to extract the physical vibration of the drone from the transmitted signal to infer the model of a drone and the load carried by it. We evaluate the DroneScale system using 5 different drone models, which carry external loads of up to 400g. The experimental results show that the system is able to estimate the external load of a drone with an average accuracy of 96.27%. We also analyze the sensitivity of the system with different load placements with respect to the drone's body, flight modes, and distances up to 200 meters.

Item Type: Conference or Workshop Item (Paper)
Date Type: Published Online
Status: Published
Schools: Computer Science & Informatics
Publisher: Association for Computing Machinery
ISBN: 978-1-4503-7590-0
Date of First Compliant Deposit: 14 August 2023
Date of Acceptance: 16 November 2020
Last Modified: 31 Aug 2023 16:15

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