Dynamic Magnetometer Calibration and Alignment to Inertial Sensors by Kalman Filtering

Wu, Yuanxin; Zou, Danping; Liu, Peilin; Wang, Yu

doi:10.1109/tcst.2017.2670527

Cited by 85 publications

(45 citation statements)

References 19 publications

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“…In the second category, the magnetometer calibration is performed with assistance from inertial sensors [18][19][20][21][22][23]. This approach has the advantage that it can perform an alignment estimation of the sensor axes between the inertial sensors and the magnetometer.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Calibration of Magnetometers Using the RLS/ML Algorithm

Cao

2020

Sensors

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This study presents a new real-time calibration algorithm for three-axis magnetometers by combining the recursive least square (RLS) estimation and maximum likelihood (ML) estimation methods. Magnetometers are widely employed to determine the heading information by sensing the magnetic field of earth; however, they are vulnerable to ambient magnetic disturbances. This makes the calibration of a magnetometer inevitable before it is employed. In this paper, first, a complete measurement error model of the magnetometer is studied, and a simplified model is developed. Then, the real-time RLS algorithm is introduced and discussed in detail, and the unbiased optimal ML is utilized to improve the accuracy of the parameter estimation. The proposed algorithm is advantageous in correcting the parameters in real time and simultaneously obtaining unbiased parameter estimation. Finally, the simulation and experimental results demonstrate that both the accuracy and computational speed of the proposed algorithm is better than those of the widely used bath-processing method. Moreover, the proposed calibration method can be adopted for calibrating other three-axis sensors.

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Section: Introductionmentioning

confidence: 99%

Real-Time Calibration of Magnetometers Using the RLS/ML Algorithm

Cao

2020

Sensors

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“…Specifically, if the sensor is placed away of magnetic disturbances, the magnitude of the measured magnetic field should be locally constant, and independent of the sensor's orientation. Similarly to the accelerometer's case, the calibration parameters are calculated by solving a minimization [8,[10][11][12][13][14] or an estimation [15] problem.…”

Section: Introductionmentioning

confidence: 99%

“…This gives rise to the need of alignment between their sensitivity axes. The authors in [15,16] use a gyroscope to align the axes of an accelerometer and a magnetometer. The authors in [17,18] exploit the fact that the measured magnetic inclination angle should be locally constant across different measurements when the accelerometer is still and the magnetometer is away of magnetic disturbances in order to align the axes of the two sensors.…”

Section: Introductionmentioning

confidence: 99%

Accelerometer and Magnetometer Joint Calibration and Axes Alignment

Papafotis

Sotiriadis

2020

Technologies

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In this work, we propose an algorithm for joint calibration and axes alignment of a 3-axis accelerometer and a 3-axis magnetometer. The proposed algorithm applies when the two sensors are fixed on the same rigid platform. It achieves accurate calibration without requiring any external piece of equipment like a turntable for the accelerometer or Gauss magnetic chamber and Maxwell coils setup for the magnetometer. The efficiency and accuracy of the proposed algorithm are evaluated using experimental data.

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“…Current results sometimes require the geomagnetic model information and needs long time (hour level) to converge to the simulated true values [11]. Recently, the concept of online calibration of magnetometer and alignment to inertial sensors has become a novel and leading tool, which can be achieved via Kalman filtering design [12] for full-parametric real-time calibration including scale factors, misalignments and biases. Simpler methods with less estimation parameters for low-cost sensors have been implemented recently using an extended Kalman filter (EKF) as well [13].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Magnetometer Disturbance Estimation via Online Nonlinear Programming

2019

IEEE Sensors J.

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Magnetometer is a significant sensor for integrated navigation. However, it suffers from many kinds of unknown dynamic magnetic disturbances. We study the problem of online estimating such disturbances via a nonlinear optimization aided by intermediate quaternion estimation from inertial fusion. The proposed optimization is constrained by geographical distribution of magnetic field forming a constrained nonlinear programming. The uniqueness of the solution has been verified mathematically and we design an interior-point-based solver for efficient computation on embedded chips. It is claimed that the designed scheme mainly outperforms in dealing with the challenging bias estimation problem under static motion as previous representatives can hardly achieve. Experimental results demonstrate the effectiveness of the proposed scheme on high accuracy, fast response and low computational load. since 2018. His research interests include inertial navigation, optimal filtering, control theory and robot vision. He is a member of IEEE.

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Dynamic Magnetometer Calibration and Alignment to Inertial Sensors by Kalman Filtering

Cited by 85 publications

References 19 publications

Real-Time Calibration of Magnetometers Using the RLS/ML Algorithm

Real-Time Calibration of Magnetometers Using the RLS/ML Algorithm

Accelerometer and Magnetometer Joint Calibration and Axes Alignment

Real-Time Magnetometer Disturbance Estimation via Online Nonlinear Programming

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