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A Small Target Localization Method Based on the Magnetic Gradient Tensor
Currently, many small target localization methods based on a magnetic gradient tensor have problems, such as complex solution processes, poor stability, and multiple solutions. This paper proposes an optimization method based on the Euler deconvolution localization method to solve these problems. In...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9607652/ https://www.ncbi.nlm.nih.gov/pubmed/36295992 http://dx.doi.org/10.3390/mi13101639 |
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author | Wang, Bo Ren, Guoquan Li, Zhining Li, Qingzhu Cai, Ziming |
author_facet | Wang, Bo Ren, Guoquan Li, Zhining Li, Qingzhu Cai, Ziming |
author_sort | Wang, Bo |
collection | PubMed |
description | Currently, many small target localization methods based on a magnetic gradient tensor have problems, such as complex solution processes, poor stability, and multiple solutions. This paper proposes an optimization method based on the Euler deconvolution localization method to solve these problems. In a simulation, the Euler deconvolution method, an improved method of the Euler deconvolution method and our proposed method are analyzed under noise conditions. These three methods are evaluated in the field with complex magnetic interference in an experiment. The simulations show that the accuracy of the proposed method is higher than that of the improved Euler deconvolution method and is slightly lower for noisy conditions. The experimental results show that the proposed method is more precise and accurate than the Euler deconvolution and enhanced methods. |
format | Online Article Text |
id | pubmed-9607652 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-96076522022-10-28 A Small Target Localization Method Based on the Magnetic Gradient Tensor Wang, Bo Ren, Guoquan Li, Zhining Li, Qingzhu Cai, Ziming Micromachines (Basel) Article Currently, many small target localization methods based on a magnetic gradient tensor have problems, such as complex solution processes, poor stability, and multiple solutions. This paper proposes an optimization method based on the Euler deconvolution localization method to solve these problems. In a simulation, the Euler deconvolution method, an improved method of the Euler deconvolution method and our proposed method are analyzed under noise conditions. These three methods are evaluated in the field with complex magnetic interference in an experiment. The simulations show that the accuracy of the proposed method is higher than that of the improved Euler deconvolution method and is slightly lower for noisy conditions. The experimental results show that the proposed method is more precise and accurate than the Euler deconvolution and enhanced methods. MDPI 2022-09-29 /pmc/articles/PMC9607652/ /pubmed/36295992 http://dx.doi.org/10.3390/mi13101639 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, Bo Ren, Guoquan Li, Zhining Li, Qingzhu Cai, Ziming A Small Target Localization Method Based on the Magnetic Gradient Tensor |
title | A Small Target Localization Method Based on the Magnetic Gradient Tensor |
title_full | A Small Target Localization Method Based on the Magnetic Gradient Tensor |
title_fullStr | A Small Target Localization Method Based on the Magnetic Gradient Tensor |
title_full_unstemmed | A Small Target Localization Method Based on the Magnetic Gradient Tensor |
title_short | A Small Target Localization Method Based on the Magnetic Gradient Tensor |
title_sort | small target localization method based on the magnetic gradient tensor |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9607652/ https://www.ncbi.nlm.nih.gov/pubmed/36295992 http://dx.doi.org/10.3390/mi13101639 |
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