Calibration of a fluxgate magnetometer array and its application in magnetic object localization

Pang, Hongfeng; Luo, Siqiang; Zhang, Qi; Li, Ji; Chen, Dixiang; Pan, Mengchun; Luo, Fei

doi:10.1088/0957-0233/24/7/075102

Cited by 47 publications

(27 citation statements)

References 27 publications

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“…Therefore, an iteration algorithm is proposed to update the magnetic moment vector and position vector with Equations (3) and (14). Due to magnetometer errors, misalignment of magnetometer array, and the distortion field, the measurement errors of magnetic gradient tensor may reach hundreds of nanoteslas [21,22,23]. Even after calibration, the residual errors of measurements are at least several nanoteslas and disturb the localization results.…”

Section: Asphericity Errors Compensation Algorithmmentioning

confidence: 99%

A Modified Magnetic Gradient Contraction Based Method for Ferromagnetic Target Localization

Wang

Zhang

et al. 2016

Sensors

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The Scalar Triangulation and Ranging (STAR) method, which is based upon the unique properties of magnetic gradient contraction, is a high real-time ferromagnetic target localization method. Only one measurement point is required in the STAR method and it is not sensitive to changes in sensing platform orientation. However, the localization accuracy of the method is limited by the asphericity errors and the inaccurate value of position leads to larger errors in the estimation of magnetic moment. To improve the localization accuracy, a modified STAR method is proposed. In the proposed method, the asphericity errors of the traditional STAR method are compensated with an iterative algorithm. The proposed method has a fast convergence rate which meets the requirement of high real-time localization. Simulations and field experiments have been done to evaluate the performance of the proposed method. The results indicate that target parameters estimated by the modified STAR method are more accurate than the traditional STAR method.

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Section: Asphericity Errors Compensation Algorithmmentioning

confidence: 99%

A Modified Magnetic Gradient Contraction Based Method for Ferromagnetic Target Localization

Wang

Zhang

et al. 2016

Sensors

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“…Fluxgate gradiometers have been proposed for space missions, and the best performance reported to date is 9.310 11 T/m in the band 0.01Hz to 10Hz [25]. Pang used the Levenberg-Marquardt algorithm to achieve calibration of single vector magnetometer and calibrated the misalignment errors considering the first magnetometer as a reference [26]. Yin constructed linear calibration method of magnetic gradient tensor system and rotated the system in three mutually perpendicular axes to calibrate the misalignment error and transformed outputs of different magnetometers into the platform frame-orthogonal coordinate [27].…”

Section: Review Of Calibration Techniquesmentioning

confidence: 99%

Two-Step Complete Calibration of Magnetic Vector Gradiometer Based on Functional Link Artificial Neural Network and Least Squares

Huang

2016

IEEE Sensors J.

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Magnetic vector gradiometers are frequently used for the detection of ferrous metals, the detection of unexploded ordinance (UXO) and defense applications. A magnetic vector gradiometer, which is under consideration in this paper, consists of two tri-axial magnetometers (TAMs). It requires a calibration procedure in order to take into account the errors in the TAM(tri-axial magnetometer) itself and the spatial misalignment of the magnetometers that will deteriorate the measurement precision of the gradiometer. This paper reports a two-step calibration algorithm of magnetic vector gradiometer based on functional link artificial neural network (FLANN) and least squares according to its response to an external magnetic vector field. The procedure and steps to identify the coefficients related to the measurement error are given. The calibration algorithm with good convergence proved by the numerical simulations decreased the relative error of magnetic vector gradient, in the case when the TAM is used in earth field, from 6.2340 down to 0.0187 and the parameters can be identified with the maximum inaccuracy of 5.35%. The efficiency of two-step calibration is also validated through experimental tests of two TAMs of type Mag03-MSB100 strapped on a nonmagnetic turntable. The calibrated coefficients are a good match with those specified by the manufacturer of the TAMs; the standard deviations of the increments of magnetic vector components in x, y and z directions decrease from 74.655nT, 90.617nT and 162.39nT before calibration down to 18.793nT, 20.095nT and 13.671nT after calibration, respectively.

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“…Thus, the calibration model can be expressed as [12]: Considering of distortion field, the error model can be expressed as: …”

Section: Magnetometer Calibration and Distortion Field Compensationmentioning

confidence: 99%

Calibration and Distortion Field Compensation of Gradiometer and the Improvement in Object Remote Sensing

Pang

et al. 2016

MATEC Web of Conferences

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Abstract. Magnetometer, misalignment error and distortion field can reduce the accuracy of gradiometers. So, it is important to calibrate and compensate gradiometers error. Scale factor, bias, nonorthogonality, misalignment and distortion field should be considered. A gradiometer is connected by an aluminium frame, which contains two fluxgate magnetometers. A nonmagnetic rotation equipment is used to change gradiometer attitude, and the compensation parameters are estimated. Experiment results show that, after calibration and compensation, error of each axis is reduced from 888.4 nT, 1292.6 nT and 168.9 nT to 15.3 nT, 22.1 nT and 9.9 nT, respectively. It shows that the proposed method can calibrate gradiometer and compensate distortion field. After calibration and compensation, the object remote sensing performance is improved.

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Calibration of a fluxgate magnetometer array and its application in magnetic object localization

Cited by 47 publications

References 27 publications

A Modified Magnetic Gradient Contraction Based Method for Ferromagnetic Target Localization

A Modified Magnetic Gradient Contraction Based Method for Ferromagnetic Target Localization

Two-Step Complete Calibration of Magnetic Vector Gradiometer Based on Functional Link Artificial Neural Network and Least Squares

Calibration and Distortion Field Compensation of Gradiometer and the Improvement in Object Remote Sensing

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