An improved magnetometer calibration and compensation method based on Levenberg–Marquardt algorithm for multi-rotor unmanned aerial vehicle

Wu, Helong; Pei, Xinbiao; Li, Jihui; Hui-bin, Gao; Bai, Yue

doi:10.1177/0020294019890627

Cited by 20 publications

(9 citation statements)

References 20 publications

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“…The key difference in Springmann's method [35] is to estimate the nine θ parameters in two separate minimization steps, as Wu et al demonstrated in [36]. We found that this two-step approach gave more consistent results for another magnetometer (not used in this work) on our UAV.…”

Section: Magnetometer Calibrationsupporting

confidence: 51%

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Fast and Noise-Resilient Magnetic Field Mapping on a Low-Cost UAV Using Gaussian Process Regression

Kuevor

Ghaffari

Atkins

et al. 2023

Sensors

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This study presents a comprehensive approach to mapping local magnetic field anomalies with robustness to magnetic noise from an unmanned aerial vehicle (UAV). The UAV collects magnetic field measurements, which are used to generate a local magnetic field map through Gaussian process regression (GPR). The research identifies two categories of magnetic noise originating from the UAV’s electronics, adversely affecting map precision. First, this paper delineates a zero-mean noise arising from high-frequency motor commands issued by the UAV’s flight controller. To mitigate this noise, the study proposes adjusting a specific gain in the vehicle’s PID controller. Next, our research reveals that the UAV generates a time-varying magnetic bias that fluctuates throughout experimental trials. To address this issue, a novel compromise mapping technique is introduced, enabling the map to learn these time-varying biases with data collected from multiple flights. The compromise map circumvents excessive computational demands without sacrificing mapping accuracy by constraining the number of prediction points used for regression. A comparative analysis of the magnetic field maps’ accuracy and the spatial density of observations employed in map construction is then conducted. This examination serves as a guideline for best practices when designing trajectories for local magnetic field mapping. Furthermore, the study presents a novel consistency metric intended to determine whether predictions from a GPR magnetic field map should be retained or discarded during state estimation. Empirical evidence from over 120 flight tests substantiates the efficacy of the proposed methodologies. The data are made publicly accessible to facilitate future research endeavors.

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Section: Magnetometer Calibrationsupporting

confidence: 51%

“…To calibrate the vehicle's magnetometer, we use the model and iterative least squares solver from [35] along with the two-step calibration procedure from [36]. The magnetometer model from [35] is repeated here…”

Section: Magnetometer Calibrationmentioning

confidence: 99%

Fast and Noise-Resilient Magnetic Field Mapping on a Low-Cost UAV Using Gaussian Process Regression

Kuevor

Ghaffari

Atkins

et al. 2023

Sensors

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show abstract

“…It is more robust than the Gauss-Newton (GN) algorithm. The Levenberg-Marquardt algorithm combines two numerical optimization algorithms: the Gradient Descent (GD) method and the Gauss-Newton (GN) method [ 41 , 42 , 43 ]. To obtain the most precise coefficients without using a highly accurate turntable system, the triaxial accelerometer should be placed continuously and fixed to cover the entire surface of the sphere, and the accelerometer should be influenced only by gravity during the experiment.…”

Section: Calibration Methodsmentioning

confidence: 99%

Thermal Calibration of Triaxial Accelerometer for Tilt Measurement

Yuan

Tang

Zhang

et al. 2023

Sensors

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The application of MEMS accelerometers used to measure inclination is constrained by their temperature dependence, and each accelerometer needs to be calibrated individually to increase stability and accuracy. This paper presents a calibration and thermal compensation method for triaxial accelerometers that aims to minimize cost and processing time while maintaining high accuracy. First, the number of positions to perform the calibration procedure is optimized based on the Levenberg-Marquardt algorithm, and then, based on this optimized calibration number, thermal compensation is performed based on the least squares method, which is necessary for environments with large temperature variations, since calibration parameters change at different temperatures. The calibration procedures and algorithms were experimentally validated on marketed accelerometers. Based on the optimized calibration method, the calibrated results achieved nearly 100 times improvement. Thermal drift calibration experiments on the triaxial accelerometer show that the thermal compensation scheme in this paper can effectively reduce drift in the temperature range of −40 °C to 60 °C. The temperature drifts of x- and y-axes are reduced from −13.2 and 11.8 mg to −0.9 and −1.1 mg, respectively. The z-axis temperature drift is reduced from −17.9 to 1.8 mg. We have conducted various experiments on the proposed calibration method and demonstrated its capacity to calibrate the sensor frame error model (SFEM) parameters. This research proposes a new low-cost and efficient strategy for increasing the practical applicability of triaxial accelerometers.

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“…It is more robust than the Gause-Newton (GN) algorithm. The Levenberg-Marquardt algorithm combines two numerical optimization algorithms: the Gradient Descent (GD) method and the Gauss-Newton (GN) method [34][35][36]. To obtain the most precise coefficients without using a highly accurate turntable system, the triaxial accelerometer should be placed continuously and fixed to cover the entire surface of the sphere, and the accelerometer should be influenced only by gravity during the experiment.…”

Section: Levenberg-marquardt Algorithmmentioning

confidence: 99%

Thermal Calibration of Triaxial Accelerometer for Tilt Measurement

Yuan¹,

Tang²,

Zhang³

et al. 2022

Preprint

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This paper presents a calibration and thermal compensation method that aims to minimize cost and processing time while maintaining high accuracy. First, the number of positions to perform the calibration procedure is optimized, and then, based on this optimized calibration number, thermal compensation is performed, which is necessary for environments with large temperature variations, since calibration parameters change at different temperatures. The calibration procedures and algorithms were experimentally validated on marketed accelerometers. Based on the optimized calibration method, the calibrated results have been obtained nearly 100 times improvement. Thermal drift calibration experiments on the triaxial accelerometer show that the thermal compensation scheme in this paper can effectively reduce drift in the temperature range of -40°C to 60°C. The temperature drifts of x- and y-axes are reduced from -13.2 and 11.8 mg to -0.9 and -1.1 mg, respectively. The z-axis temperature drift is reduced from -17.9 to 1.8 mg. We have conducted various experiments on the proposed calibration method and demonstrated its capacity to calibrate the sensor frame error model (SFEM) parameters. Therefore, the MEMS sensors calibrated in this paper have certain engineering application values for tilt measurement.

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An improved magnetometer calibration and compensation method based on Levenberg–Marquardt algorithm for multi-rotor unmanned aerial vehicle

Cited by 20 publications

References 20 publications

Fast and Noise-Resilient Magnetic Field Mapping on a Low-Cost UAV Using Gaussian Process Regression

Fast and Noise-Resilient Magnetic Field Mapping on a Low-Cost UAV Using Gaussian Process Regression

Thermal Calibration of Triaxial Accelerometer for Tilt Measurement

Thermal Calibration of Triaxial Accelerometer for Tilt Measurement

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