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Sensors 2018, 18(5), 1659; https://doi.org/10.3390/s18051659

A Nonlinear Calibration Algorithm Based on Harmonic Decomposition for Two-Axis Fluxgate Sensors

School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
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Received: 27 April 2018 / Revised: 16 May 2018 / Accepted: 18 May 2018 / Published: 22 May 2018
(This article belongs to the Section Physical Sensors)
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Abstract

Nonlinearity is a prominent limitation to the calibration performance for two-axis fluxgate sensors. In this paper, a novel nonlinear calibration algorithm taking into account the nonlinearity of errors is proposed. In order to establish the nonlinear calibration model, the combined effort of all time-invariant errors is analyzed in detail, and then harmonic decomposition method is utilized to estimate the compensation coefficients. Meanwhile, the proposed nonlinear calibration algorithm is validated and compared with a classical calibration algorithm by experiments. The experimental results show that, after the nonlinear calibration, the maximum deviation of magnetic field magnitude is decreased from 1302 nT to 30 nT, which is smaller than 81 nT after the classical calibration. Furthermore, for the two-axis fluxgate sensor used as magnetic compass, the maximum error of heading is corrected from 1.86° to 0.07°, which is approximately 11% in contrast with 0.62° after the classical calibration. The results suggest an effective way to improve the calibration performance of two-axis fluxgate sensors. View Full-Text
Keywords: fluxgate sensor; calibration algorithm; nonlinearity; harmonic decomposition fluxgate sensor; calibration algorithm; nonlinearity; harmonic decomposition
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Feng, W.; Liu, S. A Nonlinear Calibration Algorithm Based on Harmonic Decomposition for Two-Axis Fluxgate Sensors. Sensors 2018, 18, 1659.

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