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High Accuracy Passive Magnetic Field-Based Localization for Feedback Control Using Principal Component Analysis
In this paper, a novel magnetic field-based sensing system employing statistically optimized concurrent multiple sensor outputs for precise field-position association and localization is presented. This method capitalizes on the independence between simultaneous spatial field measurements at multipl...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5017445/ https://www.ncbi.nlm.nih.gov/pubmed/27529253 http://dx.doi.org/10.3390/s16081280 |
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author | Foong, Shaohui Sun, Zhenglong |
author_facet | Foong, Shaohui Sun, Zhenglong |
author_sort | Foong, Shaohui |
collection | PubMed |
description | In this paper, a novel magnetic field-based sensing system employing statistically optimized concurrent multiple sensor outputs for precise field-position association and localization is presented. This method capitalizes on the independence between simultaneous spatial field measurements at multiple locations to induce unique correspondences between field and position. This single-source-multi-sensor configuration is able to achieve accurate and precise localization and tracking of translational motion without contact over large travel distances for feedback control. Principal component analysis (PCA) is used as a pseudo-linear filter to optimally reduce the dimensions of the multi-sensor output space for computationally efficient field-position mapping with artificial neural networks (ANNs). Numerical simulations are employed to investigate the effects of geometric parameters and Gaussian noise corruption on PCA assisted ANN mapping performance. Using a 9-sensor network, the sensing accuracy and closed-loop tracking performance of the proposed optimal field-based sensing system is experimentally evaluated on a linear actuator with a significantly more expensive optical encoder as a comparison. |
format | Online Article Text |
id | pubmed-5017445 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-50174452016-09-22 High Accuracy Passive Magnetic Field-Based Localization for Feedback Control Using Principal Component Analysis Foong, Shaohui Sun, Zhenglong Sensors (Basel) Article In this paper, a novel magnetic field-based sensing system employing statistically optimized concurrent multiple sensor outputs for precise field-position association and localization is presented. This method capitalizes on the independence between simultaneous spatial field measurements at multiple locations to induce unique correspondences between field and position. This single-source-multi-sensor configuration is able to achieve accurate and precise localization and tracking of translational motion without contact over large travel distances for feedback control. Principal component analysis (PCA) is used as a pseudo-linear filter to optimally reduce the dimensions of the multi-sensor output space for computationally efficient field-position mapping with artificial neural networks (ANNs). Numerical simulations are employed to investigate the effects of geometric parameters and Gaussian noise corruption on PCA assisted ANN mapping performance. Using a 9-sensor network, the sensing accuracy and closed-loop tracking performance of the proposed optimal field-based sensing system is experimentally evaluated on a linear actuator with a significantly more expensive optical encoder as a comparison. MDPI 2016-08-12 /pmc/articles/PMC5017445/ /pubmed/27529253 http://dx.doi.org/10.3390/s16081280 Text en © 2016 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 (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Foong, Shaohui Sun, Zhenglong High Accuracy Passive Magnetic Field-Based Localization for Feedback Control Using Principal Component Analysis |
title | High Accuracy Passive Magnetic Field-Based Localization for Feedback Control Using Principal Component Analysis |
title_full | High Accuracy Passive Magnetic Field-Based Localization for Feedback Control Using Principal Component Analysis |
title_fullStr | High Accuracy Passive Magnetic Field-Based Localization for Feedback Control Using Principal Component Analysis |
title_full_unstemmed | High Accuracy Passive Magnetic Field-Based Localization for Feedback Control Using Principal Component Analysis |
title_short | High Accuracy Passive Magnetic Field-Based Localization for Feedback Control Using Principal Component Analysis |
title_sort | high accuracy passive magnetic field-based localization for feedback control using principal component analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5017445/ https://www.ncbi.nlm.nih.gov/pubmed/27529253 http://dx.doi.org/10.3390/s16081280 |
work_keys_str_mv | AT foongshaohui highaccuracypassivemagneticfieldbasedlocalizationforfeedbackcontrolusingprincipalcomponentanalysis AT sunzhenglong highaccuracypassivemagneticfieldbasedlocalizationforfeedbackcontrolusingprincipalcomponentanalysis |