Mapping the magnetic field using a magnetometer array with noisy input Gaussian process regression

Thomas Edridge; Manon Kok

doi:10.23919/FUSION52260.2023.10224116

Mapping the magnetic field using a magnetometer array with noisy input Gaussian process regression

^*Corresponding author for this work

Team Manon Kok

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

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Abstract

Ferromagnetic materials in indoor environments give rise to disturbances in the ambient magnetic field. Maps of these magnetic disturbances can be used for indoor localisation. A Gaussian process can be used to learn the spatially varying magnitude of the magnetic field using magnetometer measurements and information about the position of the magnetometer. The position of the magnetometer, however, is frequently only approximately known. This negatively affects the quality of the magnetic field map. In this paper, we investigate how an array of magnetometers can be used to improve the quality of the magnetic field map. The position of the array is approximately known, but the relative locations of the magnetometers on the array are known. We include this information in a novel method to make a map of the ambient magnetic field. We study the properties of our method in simulation and show that our method improves the map quality. We also demonstrate the efficacy of our method with experimental data for the mapping of the magnetic field using an array of 30 magnetometers.

Original language	English
Title of host publication	Proceedings of the 2023 26th International Conference on Information Fusion, FUSION 2023
Publisher	IEEE
Number of pages	7
ISBN (Electronic)	979-8-8903-4485-4
DOIs	https://doi.org/10.23919/FUSION52260.2023.10224116
Publication status	Published - 2023
Event	26th International Conference on Information Fusion, FUSION 2023 - Charleston, United States Duration: 27 Jun 2023 → 30 Jun 2023

Conference

Conference	26th International Conference on Information Fusion, FUSION 2023
Country/Territory	United States
City	Charleston
Period	27/06/23 → 30/06/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

Gaussian process
magnetic field
mapping
noisy inputs
sensor array

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10.23919/FUSION52260.2023.10224116

Mapping_the_magnetic_field_using_a_magnetometer_array_with_noisy_input_Gaussian_process_regressionFinal published version, 1.21 MB

Cite this

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title = "Mapping the magnetic field using a magnetometer array with noisy input Gaussian process regression",

abstract = "Ferromagnetic materials in indoor environments give rise to disturbances in the ambient magnetic field. Maps of these magnetic disturbances can be used for indoor localisation. A Gaussian process can be used to learn the spatially varying magnitude of the magnetic field using magnetometer measurements and information about the position of the magnetometer. The position of the magnetometer, however, is frequently only approximately known. This negatively affects the quality of the magnetic field map. In this paper, we investigate how an array of magnetometers can be used to improve the quality of the magnetic field map. The position of the array is approximately known, but the relative locations of the magnetometers on the array are known. We include this information in a novel method to make a map of the ambient magnetic field. We study the properties of our method in simulation and show that our method improves the map quality. We also demonstrate the efficacy of our method with experimental data for the mapping of the magnetic field using an array of 30 magnetometers. ",

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note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; 26th International Conference on Information Fusion, FUSION 2023 ; Conference date: 27-06-2023 Through 30-06-2023",

year = "2023",

doi = "10.23919/FUSION52260.2023.10224116",

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booktitle = "Proceedings of the 2023 26th International Conference on Information Fusion, FUSION 2023",

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Edridge, T & Kok, M 2023, Mapping the magnetic field using a magnetometer array with noisy input Gaussian process regression. in Proceedings of the 2023 26th International Conference on Information Fusion, FUSION 2023. IEEE, 26th International Conference on Information Fusion, FUSION 2023, Charleston, United States, 27/06/23. https://doi.org/10.23919/FUSION52260.2023.10224116

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AU - Edridge, Thomas

AU - Kok, Manon

N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2023

Y1 - 2023

N2 - Ferromagnetic materials in indoor environments give rise to disturbances in the ambient magnetic field. Maps of these magnetic disturbances can be used for indoor localisation. A Gaussian process can be used to learn the spatially varying magnitude of the magnetic field using magnetometer measurements and information about the position of the magnetometer. The position of the magnetometer, however, is frequently only approximately known. This negatively affects the quality of the magnetic field map. In this paper, we investigate how an array of magnetometers can be used to improve the quality of the magnetic field map. The position of the array is approximately known, but the relative locations of the magnetometers on the array are known. We include this information in a novel method to make a map of the ambient magnetic field. We study the properties of our method in simulation and show that our method improves the map quality. We also demonstrate the efficacy of our method with experimental data for the mapping of the magnetic field using an array of 30 magnetometers.

AB - Ferromagnetic materials in indoor environments give rise to disturbances in the ambient magnetic field. Maps of these magnetic disturbances can be used for indoor localisation. A Gaussian process can be used to learn the spatially varying magnitude of the magnetic field using magnetometer measurements and information about the position of the magnetometer. The position of the magnetometer, however, is frequently only approximately known. This negatively affects the quality of the magnetic field map. In this paper, we investigate how an array of magnetometers can be used to improve the quality of the magnetic field map. The position of the array is approximately known, but the relative locations of the magnetometers on the array are known. We include this information in a novel method to make a map of the ambient magnetic field. We study the properties of our method in simulation and show that our method improves the map quality. We also demonstrate the efficacy of our method with experimental data for the mapping of the magnetic field using an array of 30 magnetometers.

KW - Gaussian process

KW - magnetic field

KW - mapping

KW - noisy inputs

KW - sensor array

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M3 - Conference contribution

AN - SCOPUS:85171557030

BT - Proceedings of the 2023 26th International Conference on Information Fusion, FUSION 2023

PB - IEEE

T2 - 26th International Conference on Information Fusion, FUSION 2023

Y2 - 27 June 2023 through 30 June 2023

ER -

Mapping the magnetic field using a magnetometer array with noisy input Gaussian process regression

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