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Artificial Vector Calibration Method for Differencing Magnetic Gradient Tensor Systems

The measurement error of the differencing (i.e., using two homogenous field sensors at a known baseline distance) magnetic gradient tensor system includes the biases, scale factors, nonorthogonality of the single magnetic sensor, and the misalignment error between the sensor arrays, all of which can...

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Detalles Bibliográficos
Autores principales: Li, Qingzhu, Li, Zhining, Zhang, Yingtang, Yin, Gang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5855530/
https://www.ncbi.nlm.nih.gov/pubmed/29373544
http://dx.doi.org/10.3390/s18020361
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author Li, Qingzhu
Li, Zhining
Zhang, Yingtang
Yin, Gang
author_facet Li, Qingzhu
Li, Zhining
Zhang, Yingtang
Yin, Gang
author_sort Li, Qingzhu
collection PubMed
description The measurement error of the differencing (i.e., using two homogenous field sensors at a known baseline distance) magnetic gradient tensor system includes the biases, scale factors, nonorthogonality of the single magnetic sensor, and the misalignment error between the sensor arrays, all of which can severely affect the measurement accuracy. In this paper, we propose a low-cost artificial vector calibration method for the tensor system. Firstly, the error parameter linear equations are constructed based on the single-sensor’s system error model to obtain the artificial ideal vector output of the platform, with the total magnetic intensity (TMI) scalar as a reference by two nonlinear conversions, without any mathematical simplification. Secondly, the Levenberg–Marquardt algorithm is used to compute the integrated model of the 12 error parameters by nonlinear least-squares fitting method with the artificial vector output as a reference, and a total of 48 parameters of the system is estimated simultaneously. The calibrated system outputs along the reference platform-orthogonal coordinate system. The analysis results show that the artificial vector calibrated output can track the orientation fluctuations of TMI accurately, effectively avoiding the “overcalibration” problem. The accuracy of the error parameters’ estimation in the simulation is close to 100%. The experimental root-mean-square error (RMSE) of the TMI and tensor components is less than 3 nT and 20 nT/m, respectively, and the estimation of the parameters is highly robust.
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spelling pubmed-58555302018-03-20 Artificial Vector Calibration Method for Differencing Magnetic Gradient Tensor Systems Li, Qingzhu Li, Zhining Zhang, Yingtang Yin, Gang Sensors (Basel) Article The measurement error of the differencing (i.e., using two homogenous field sensors at a known baseline distance) magnetic gradient tensor system includes the biases, scale factors, nonorthogonality of the single magnetic sensor, and the misalignment error between the sensor arrays, all of which can severely affect the measurement accuracy. In this paper, we propose a low-cost artificial vector calibration method for the tensor system. Firstly, the error parameter linear equations are constructed based on the single-sensor’s system error model to obtain the artificial ideal vector output of the platform, with the total magnetic intensity (TMI) scalar as a reference by two nonlinear conversions, without any mathematical simplification. Secondly, the Levenberg–Marquardt algorithm is used to compute the integrated model of the 12 error parameters by nonlinear least-squares fitting method with the artificial vector output as a reference, and a total of 48 parameters of the system is estimated simultaneously. The calibrated system outputs along the reference platform-orthogonal coordinate system. The analysis results show that the artificial vector calibrated output can track the orientation fluctuations of TMI accurately, effectively avoiding the “overcalibration” problem. The accuracy of the error parameters’ estimation in the simulation is close to 100%. The experimental root-mean-square error (RMSE) of the TMI and tensor components is less than 3 nT and 20 nT/m, respectively, and the estimation of the parameters is highly robust. MDPI 2018-01-26 /pmc/articles/PMC5855530/ /pubmed/29373544 http://dx.doi.org/10.3390/s18020361 Text en © 2018 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
Li, Qingzhu
Li, Zhining
Zhang, Yingtang
Yin, Gang
Artificial Vector Calibration Method for Differencing Magnetic Gradient Tensor Systems
title Artificial Vector Calibration Method for Differencing Magnetic Gradient Tensor Systems
title_full Artificial Vector Calibration Method for Differencing Magnetic Gradient Tensor Systems
title_fullStr Artificial Vector Calibration Method for Differencing Magnetic Gradient Tensor Systems
title_full_unstemmed Artificial Vector Calibration Method for Differencing Magnetic Gradient Tensor Systems
title_short Artificial Vector Calibration Method for Differencing Magnetic Gradient Tensor Systems
title_sort artificial vector calibration method for differencing magnetic gradient tensor systems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5855530/
https://www.ncbi.nlm.nih.gov/pubmed/29373544
http://dx.doi.org/10.3390/s18020361
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