A Compressed Sensing Algorithm for Magnetic Dipole Localization
article
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 ℓ1 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.
Topics
Compressed sensingmagnetic sensorssensor systems and applicationsAnomaly detectionCompressed sensingIterative methodsLocationMagnetic fieldsMagnetic momentsMagnetometersCompressed-SensingDipole localizationMagnetic anomaly detectionMagnetic fields sensorsNoisy measurementsOptimisationsSensing algorithmsSensor systems and applicationsSparse representationMagnetic resonance imaging
TNO Identifier
980694
ISSN
1530437X
Source
IEEE Sensors Journal, 22(15), pp. 14825-14833.
Publisher
Institute of Electrical and Electronics Engineers Inc.
Pages
14825-14833
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