Print Email Facebook Twitter A Compressed Sensing Algorithm for Magnetic Dipole Localization Title A Compressed Sensing Algorithm for Magnetic Dipole Localization Author de Gijsel, Stefan L. (TNO) Vijn, A.R.P.J. (TU Delft Mathematical Physics; TNO) Tan, Reinier G. (TNO) Date 2022 Abstract This paper proposes an algorithm to localize a magnetic dipole using a limited number of noisy measurements from magnetic field sensors. The algorithm is based on the theory of compressed sensing, and exploits the sparseness of the magnetic dipole in space. Beforehand, a basis consisting of magnetic dipole fields belonging to individual dipoles in an evenly spaced 3D grid within a specified search domain is constructed. In the algorithm, a number of sensors is chosen which measure all three magnetic field components. The sensors are chosen optimally using QR pivoting. Using the pre-constructed basis and the obtained field measurements, a sparse representation in the location domain is computed using $\ell _{{1}}$ optimization. Based on the resulting sparse representation, the location and magnetic moment of the magnetic dipole are estimated. An extension to an iterative method is implemented, where the basis and chosen sensors improve after every location estimate. Numerical simulations have been performed to verify the algorithm, and experiments have been done for validation. The proposed algorithm is shown to be effective in localizing magnetic dipoles. Subject Compressed sensingmagnetic anomaly detectionmagnetic sensorssensor systems and applications To reference this document use: http://resolver.tudelft.nl/uuid:a6d1671b-9705-4b23-8226-524ad6b02f5b DOI https://doi.org/10.1109/JSEN.2022.3184814 Embargo date 2023-07-01 ISSN 1558-1748 Source IEEE Sensors Journal, 22 (15), 14825-14833 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. Part of collection Institutional Repository Document type journal article Rights © 2022 Stefan L. de Gijsel, A.R.P.J. Vijn, Reinier G. Tan Files PDF A_Compressed_Sensing_Algo ... zation.pdf 1.74 MB Close viewer /islandora/object/uuid:a6d1671b-9705-4b23-8226-524ad6b02f5b/datastream/OBJ/view