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Fast Localization and Characterization of Underground Targets with a Towed Transient Electromagnetic Array System

A fast inversion algorithm combined with the transient electromagnetic (TEM) detection system has important significance for improving the detection efficiency of unexploded ordnance. The traditional algorithms, such as differential evolution or Gauss–Newton algorithms, usually require tens to thous...

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Autores principales: Wang, Lijie, Zhang, Shuang, Chen, Shudong, Luo, Chaopeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8878260/
https://www.ncbi.nlm.nih.gov/pubmed/35214550
http://dx.doi.org/10.3390/s22041648
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author Wang, Lijie
Zhang, Shuang
Chen, Shudong
Luo, Chaopeng
author_facet Wang, Lijie
Zhang, Shuang
Chen, Shudong
Luo, Chaopeng
author_sort Wang, Lijie
collection PubMed
description A fast inversion algorithm combined with the transient electromagnetic (TEM) detection system has important significance for improving the detection efficiency of unexploded ordnance. The traditional algorithms, such as differential evolution or Gauss–Newton algorithms, usually require tens to thousands of iterations to locate the underground target. A new algorithm with a magnetic gradient tensor and singular value decomposition (SVD) to estimate the target position and characterization quickly and accurately is proposed in this paper. Two modes of magnetic gradient tensor are constructed to accurately locate shallow and deep targets, respectively. The SVD algorithm is applied to the responses to estimate the electromagnetic characteristics of the target quickly and accurately. To verify the performance of the proposed algorithm, a towed TEM sensor is designed, which is constructed with three transmitting coils and nine three-component receiving coils arranged in a 3 × 3 array. Field experiments in survey and cued modes were taken to verify the performance of the proposed algorithm and the towed system. Results show that the magnetic gradient tensor algorithm proposed in this paper can accurately locate a single target within 2.0 m depth, and the error of depth is no more than 8 cm. Even for overlapping response of multi targets, the error of depth is no more than 12 cm. The underground target can be accurately characterized by the SVD algorithm. For targets with depths over 2.0 m, the signal-to-noise ratio of characteristic response estimated by SVD is higher than that of the traditional method. The proposed method needs approximately 40 ms, only 1% of the traditional one, considerably improving detection efficiency and laying a theoretical and experimental foundation for real-time data processing.
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spelling pubmed-88782602022-02-26 Fast Localization and Characterization of Underground Targets with a Towed Transient Electromagnetic Array System Wang, Lijie Zhang, Shuang Chen, Shudong Luo, Chaopeng Sensors (Basel) Article A fast inversion algorithm combined with the transient electromagnetic (TEM) detection system has important significance for improving the detection efficiency of unexploded ordnance. The traditional algorithms, such as differential evolution or Gauss–Newton algorithms, usually require tens to thousands of iterations to locate the underground target. A new algorithm with a magnetic gradient tensor and singular value decomposition (SVD) to estimate the target position and characterization quickly and accurately is proposed in this paper. Two modes of magnetic gradient tensor are constructed to accurately locate shallow and deep targets, respectively. The SVD algorithm is applied to the responses to estimate the electromagnetic characteristics of the target quickly and accurately. To verify the performance of the proposed algorithm, a towed TEM sensor is designed, which is constructed with three transmitting coils and nine three-component receiving coils arranged in a 3 × 3 array. Field experiments in survey and cued modes were taken to verify the performance of the proposed algorithm and the towed system. Results show that the magnetic gradient tensor algorithm proposed in this paper can accurately locate a single target within 2.0 m depth, and the error of depth is no more than 8 cm. Even for overlapping response of multi targets, the error of depth is no more than 12 cm. The underground target can be accurately characterized by the SVD algorithm. For targets with depths over 2.0 m, the signal-to-noise ratio of characteristic response estimated by SVD is higher than that of the traditional method. The proposed method needs approximately 40 ms, only 1% of the traditional one, considerably improving detection efficiency and laying a theoretical and experimental foundation for real-time data processing. MDPI 2022-02-20 /pmc/articles/PMC8878260/ /pubmed/35214550 http://dx.doi.org/10.3390/s22041648 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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/).
spellingShingle Article
Wang, Lijie
Zhang, Shuang
Chen, Shudong
Luo, Chaopeng
Fast Localization and Characterization of Underground Targets with a Towed Transient Electromagnetic Array System
title Fast Localization and Characterization of Underground Targets with a Towed Transient Electromagnetic Array System
title_full Fast Localization and Characterization of Underground Targets with a Towed Transient Electromagnetic Array System
title_fullStr Fast Localization and Characterization of Underground Targets with a Towed Transient Electromagnetic Array System
title_full_unstemmed Fast Localization and Characterization of Underground Targets with a Towed Transient Electromagnetic Array System
title_short Fast Localization and Characterization of Underground Targets with a Towed Transient Electromagnetic Array System
title_sort fast localization and characterization of underground targets with a towed transient electromagnetic array system
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8878260/
https://www.ncbi.nlm.nih.gov/pubmed/35214550
http://dx.doi.org/10.3390/s22041648
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