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Open AccessArticle

Radioactive Source Localisation via Projective Linear Reconstruction

1
HH Wills Physics Laboratory, School of Physics, University of Bristol, Tyndall Avenue, Bristol BS8 1TL, UK
2
Department of Aerospace Engineering, University of Bristol, Queens Building, University Walk, Bristol BS8 1TR, UK
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(3), 807; https://doi.org/10.3390/s21030807
Received: 19 November 2020 / Revised: 20 January 2021 / Accepted: 22 January 2021 / Published: 26 January 2021
(This article belongs to the Section Remote Sensors)
Radiation mapping, through the detection of ionising gamma-ray emissions, is an important technique used across the nuclear industry to characterise environments over a range of length scales. In complex scenarios, the precise localisation and activity of radiological sources becomes difficult to determine due to the inability to directly image gamma photon emissions. This is a result of the potentially unknown number of sources combined with uncertainties associated with the source-detector separation—causing an apparent ‘blurring’ of the as-detected radiation field relative to the true distribution. Accurate delimitation of distinct sources is important for decommissioning, waste processing, and homeland security. Therefore, methods for estimating the precise, ‘true’ solution from radiation mapping measurements are required. Herein is presented a computational method of enhanced radiological source localisation from scanning survey measurements conducted with a robotic arm. The procedure uses an experimentally derived Detector Response Function (DRF) to perform a randomised-Kaczmarz deconvolution from robotically acquired radiation field measurements. The performance of the process is assessed on radiation maps obtained from a series of emulated waste processing scenarios. The results demonstrate a Projective Linear Reconstruction (PLR) algorithm can successfully locate a series of point sources to within 2 cm of the true locations, corresponding to resolution enhancements of between 5× and 10×. View Full-Text
Keywords: radiation sensing; micro-gamma spectrometers; localisation; robotics sensing; radiation mapping; linear inversion; inverse problems radiation sensing; micro-gamma spectrometers; localisation; robotics sensing; radiation mapping; linear inversion; inverse problems
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MDPI and ACS Style

White, S.R.; Wood, K.T.; Martin, P.G.; Connor, D.T.; Scott, T.B.; Megson-Smith, D.A. Radioactive Source Localisation via Projective Linear Reconstruction. Sensors 2021, 21, 807. https://doi.org/10.3390/s21030807

AMA Style

White SR, Wood KT, Martin PG, Connor DT, Scott TB, Megson-Smith DA. Radioactive Source Localisation via Projective Linear Reconstruction. Sensors. 2021; 21(3):807. https://doi.org/10.3390/s21030807

Chicago/Turabian Style

White, Samuel R.; Wood, Kieran T.; Martin, Peter G.; Connor, Dean T.; Scott, Thomas B.; Megson-Smith, David A. 2021. "Radioactive Source Localisation via Projective Linear Reconstruction" Sensors 21, no. 3: 807. https://doi.org/10.3390/s21030807

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