A New Calibration Method of Three Axis Magnetometer With Nonlinearity Suppression

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

doi:10.1109/tmag.2013.2259842

Cited by 34 publications

(14 citation statements)

References 29 publications

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“…The required measures may include the use of non-magnetic materials, electronic board design with smallest possible current loops, using twisted pair wires whenever possible, etc. In addition, calibration of the magnetometer may compensate for residual noise and sensor imperfections such as small misorthogonalities [16][17][18][19]46]. It is also desirable that the magnetometer acquisition unit will support a wider frequency bandwidth allowing smarter beacon modulation.…”

Section: Further Investigationmentioning

confidence: 99%

“…Traditional approaches for magnetic indoor navigation have been focused on the DC magnetic field. A well-known application is a compass which relies on the Earth's magnetic field for heading [14][15][16][17][18][19][20][21]. Navigation can be performed by combining a compass with an inertial navigation system (INS), or the dead reckoning technique [19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

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A method for indoor navigation based on magnetic beacons using smartphones and tablets

Sheinker

Ginzburg²,

Salomonski³

et al. 2016

Measurement

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Section: Further Investigationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A method for indoor navigation based on magnetic beacons using smartphones and tablets

Sheinker

Ginzburg²,

Salomonski³

et al. 2016

Measurement

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“…Errors due to temperature and nonlinearity are prominent limitations to the calibrated performance of magnetometers, and they should be dealt respectively and cautiously. Pang dealt well with these two kinds of errors [29,30]. We build the mathematical model of magnetic vector gradiometer without considering these factors in this paper.…”

Section: Fig1 Spatial Relations Of Sensor Sensitive Axes For Vector mentioning

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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“…We found the magnetometer in the IMU to have a nonlinear response ( Fig. 4) leading to an error of 5 to 10 degrees in azimuth pointing due to cross-field effects and hysteresis [Pang et al, 2013]. To measure the nonlinearity, the sensor was mounted on a 3.5-inch Meade ETX 90 reflector telescope which was rotated by 360 degrees.…”

Section: Magnetometer Calibrationmentioning

confidence: 99%

A Raspberry Pi-Based Attitude Sensor

Sreejith

Mathew

Sarpotdar

et al. 2014

J. Astron. Instrum.

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We have developed a lightweight low-cost attitude sensor, based on a Raspberry Pi, built with readily available commercial components. It can be used in experiments where weight and power are constrained, such as in high- altitude lightweight balloon flights. This attitude sensor will be used as a major building block in a closed-loop control system with driver motors to stabilize and point cameras and telescopes for astronomical observations from a balloon-borne payload.Comment: Journal of Astronomical Instrumentation, Volume 03, Issue 02, November 201

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A New Calibration Method of Three Axis Magnetometer With Nonlinearity Suppression

Cited by 34 publications

References 29 publications

A method for indoor navigation based on magnetic beacons using smartphones and tablets

A method for indoor navigation based on magnetic beacons using smartphones and tablets

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

A Raspberry Pi-Based Attitude Sensor

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