Magnetometer Calibration Using Inertial Sensors

Kok, Manon; Schön, Thomas B.

doi:10.1109/jsen.2016.2569160

Cited by 148 publications

(75 citation statements)

References 24 publications

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“…In these cases, it is important that the inertial sensor axes and the axes of the additional sensors are aligned. Examples include using inertial sensors in combination with magnetometers [75,126,15] and with cameras [60,83,98].…”

Section: Chapter 5 Calibrationmentioning

confidence: 99%

Using Inertial Sensors for Position and Orientation Estimation

Kok

Hol

Schön

2017

FNT in Signal Processing

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309

287

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In recent years, microelectromechanical system (MEMS) inertial sensors (3D accelerometers and 3D gyroscopes) have become widely available due to their small size and low cost. Inertial sensor measurements are obtained at high sampling rates and can be integrated to obtain position and orientation information. These estimates are accurate on a short time scale, but suffer from integration drift over longer time scales. To overcome this issue, inertial sensors are typically combined with additional sensors and models. In this tutorial we focus on the signal processing aspects of position and orientation estimation using inertial sensors. We discuss different modeling choices and a selected number of important algorithms. The algorithms include optimization-based smoothing and filtering as well as computationally cheaper extended Kalman filter and complementary filter implementations. The quality of their estimates is illustrated using both experimental and simulated data.

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Section: Chapter 5 Calibrationmentioning

confidence: 99%

Using Inertial Sensors for Position and Orientation Estimation

Kok

Hol

Schön

2017

FNT in Signal Processing

Self Cite

309

287

View full text Add to dashboard Cite

show abstract

“…In this paper, we are not going to the details of the sensor hard-iron and soft-iron calibration procedure. We assume the measured magnetic field B Q p,k is calibrated by using the method described in [14]. Additional to the static magnetic deviation error which can be addressed by applying the offline calibration, the dynamic magnetic deviation error is still problematic and subsists as the most dominant error source.…”

Section: Skewed-redundant Magnetometer Fusionmentioning

confidence: 99%

“…Determining error-free and reliable heading estimation with respect to a known reference is problematic in case of indoor applications. This is mainly because of different sources of errors in the MEMS-based magnetometer mea-surements [14]- [17]. The most dominant source of error for such a magnetometer sensor, however, is the interference of the external magnetic field in an indoor environment.…”

Section: Introductionmentioning

confidence: 99%

“…Permanent magnets and ferromagnetic materials are the main cause of such a magnetic perturbation. In this regard, calibration of the MEMS-based magnetometers for heading estimation has been investigated thoroughly during the last decades [14], [18]. Probabilistic models for sensor calibration and fusion such as the Unscented Kalman Filter (UKF), the Extended Kalman Filter (EKF), and Ellipsoid Fitting are well known in this field [19]- [22].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Skewed-redundant Hall-effect Magnetic Sensor Fusion for Perturbation-free Indoor Heading Estimation

Karimi¹,

Babaians²,

Oelsch³

et al. 2020

Preprint

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Robust attitude and heading estimation with respect to a known reference is an essential component for indoor localization in robotic applications. Affordable Attitude and Heading Reference Systems (AHRS) are typically using 9-axis solid-state MEMS-based sensors. The accuracy of heading estimation on such a system depends on the Earth's magnetic field measurement accuracy. The measurement of the Earth's magnetic field using MEMS-based magnetometer sensors in an indoor environment, however, is strongly affected by external magnetic perturbations. This paper presents a novel approach for robust indoor heading estimation based on skewed-redundant magnetometer fusion. A tetrahedron platform based on Hall-effect magnetic sensors is designed to determine the Earth's magnetic field with the ability to compensate for external magnetic field anomalies. Additionally, a correlation-based fusion technique is introduced for perturbation mitigation using the proposed skewed-redundant configuration. The proposed fusion technique uses a correlation coefficient analysis for determining the distorted axis and extracts the perturbation-free Earth's magnetic field vector from the redundant magnetic measurement. Our experimental results show that the proposed scheme is able to successfully mitigate the anomalies in the magnetic field measurement and estimates the Earth's true magnetic field. Using the proposed platform, we achieve a Root Mean Square Error of 12.74$\degree$ for indoor heading estimation without using an additional gyroscope.

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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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Magnetometer Calibration Using Inertial Sensors

Cited by 148 publications

References 24 publications

Using Inertial Sensors for Position and Orientation Estimation

Using Inertial Sensors for Position and Orientation Estimation

Skewed-redundant Hall-effect Magnetic Sensor Fusion for Perturbation-free Indoor Heading Estimation

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

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