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 |
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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 | |
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 |
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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-careOtherwise 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