A Compressed Sensing Algorithm for Magnetic Dipole Localization

Stefan L. de Gijsel*, Aad R.P. J. Vijn, Reinier G. Tan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number9807646
Pages (from-to)14825-14833
Number of pages9
JournalIEEE Sensors Journal
Volume22
Issue number15
DOIs
Publication statusPublished - 2022

Keywords

  • Compressed sensing
  • magnetic anomaly detection
  • magnetic sensors
  • sensor systems and applications

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