Radioactive Source Localisation via Projective Linear Reconstruction
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 de...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/3/807 |
| _version_ | 1797407315158106112 |
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| author | Samuel R. White Kieran T. Wood Peter G. Martin Dean T. Connor Thomas B. Scott David A. Megson-Smith |
| author_facet | Samuel R. White Kieran T. Wood Peter G. Martin Dean T. Connor Thomas B. Scott David A. Megson-Smith |
| author_sort | Samuel R. White |
| collection | DOAJ |
| description | 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×. |
| first_indexed | 2024-03-09T03:39:34Z |
| format | Article |
| id | doaj.art-88a540508f084f6c89a3b3ea5d5d6563 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T03:39:34Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-88a540508f084f6c89a3b3ea5d5d65632023-12-03T14:43:12ZengMDPI AGSensors1424-82202021-01-0121380710.3390/s21030807Radioactive Source Localisation via Projective Linear ReconstructionSamuel R. White0Kieran T. Wood1Peter G. Martin2Dean T. Connor3Thomas B. Scott4David A. Megson-Smith5HH Wills Physics Laboratory, School of Physics, University of Bristol, Tyndall Avenue, Bristol BS8 1TL, UKDepartment of Aerospace Engineering, University of Bristol, Queens Building, University Walk, Bristol BS8 1TR, UKHH Wills Physics Laboratory, School of Physics, University of Bristol, Tyndall Avenue, Bristol BS8 1TL, UKHH Wills Physics Laboratory, School of Physics, University of Bristol, Tyndall Avenue, Bristol BS8 1TL, UKHH Wills Physics Laboratory, School of Physics, University of Bristol, Tyndall Avenue, Bristol BS8 1TL, UKHH Wills Physics Laboratory, School of Physics, University of Bristol, Tyndall Avenue, Bristol BS8 1TL, UKRadiation 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×.https://www.mdpi.com/1424-8220/21/3/807radiation sensingmicro-gamma spectrometerslocalisationrobotics sensingradiation mappinglinear inversion |
| spellingShingle | Samuel R. White Kieran T. Wood Peter G. Martin Dean T. Connor Thomas B. Scott David A. Megson-Smith Radioactive Source Localisation via Projective Linear Reconstruction Sensors radiation sensing micro-gamma spectrometers localisation robotics sensing radiation mapping linear inversion |
| title | Radioactive Source Localisation via Projective Linear Reconstruction |
| title_full | Radioactive Source Localisation via Projective Linear Reconstruction |
| title_fullStr | Radioactive Source Localisation via Projective Linear Reconstruction |
| title_full_unstemmed | Radioactive Source Localisation via Projective Linear Reconstruction |
| title_short | Radioactive Source Localisation via Projective Linear Reconstruction |
| title_sort | radioactive source localisation via projective linear reconstruction |
| topic | radiation sensing micro-gamma spectrometers localisation robotics sensing radiation mapping linear inversion |
| url | https://www.mdpi.com/1424-8220/21/3/807 |
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