Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/38037
Title: Radiological Surveillance Using a Fixed-Wing UAV Platform
Authors: GEELEN, Stef 
CAMPS, Johan 
Olyslaegers, Geert
Ilegems, Greet
SCHROEYERS, Wouter 
Issue Date: 2022
Publisher: MDPI
Source: Remote sensing (Basel), 14 (16) (Art N° 3908)
Abstract: A drone-detector system was designed, developed, and tested for radiological monitoring. The system was tailored to perform measurements during the threat, release, and post-release phases of a nuclear or radiological event. This allows the surveillance of large areas, with an autonomy of up to 12 h, in a large range of altitudes above ground level. The detector system was optimized for gamma spectroscopy, taking into account the available payload for maximum endurance and maximum detection efficiency using 'PENELOPE (2018)' Monte Carlo simulations. A generic methodology was used to derive quantitative information on radioactivity levels from the raw measured gamma-ray spectra at different altitudes. Based on the methodology, it was demonstrated that the drone-detector system can measure the concentration of potassium-40 (K-40) that is naturally present in the soil. These measurements complied within 30% of the soil sampling results taking into account the uncertainties. The functioning of the system was tested during test flights, which demonstrated that radionuclide identification and quantification of radioactivity concentrations are possible.
Notes: Geelen, S (corresponding author), SCK CEN Belgian Nucl Res Ctr, Boeretang 200, B-2400 Mol, Belgium.; Geelen, S (corresponding author), Hasselt Univ, Nucl Technol Fac Engn Technol, NuTeC, CMK, Agoralaan Bldg H, B-3590 Diepenbeek, Belgium.
stef.geelen@sckcen.be
Keywords: UAV;drones;radiological monitoring;fixed-wing;detector optimization
Document URI: http://hdl.handle.net/1942/38037
e-ISSN: 2072-4292
DOI: 10.3390/rs14163908
ISI #: 000845304000001
Rights: 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).
Category: A1
Type: Journal Contribution
Validations: ecoom 2023
Appears in Collections:Research publications

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