Calibration Method For Magnetometers Research Articles

Calibration of strapdown magnetic vector measurement systems based on a plane compression method

The strapdown magnetic vector measurement system, which can measure the magnetic vector and the attitude of a magnetometer simultaneously, has wide applications in geophysical prospecting, etc. Calibration of systematic errors, including magnetometer errors and misalignment errors, is essential for this system. Traditional methods calibrate these two errors separately, with the problem of cumbersome steps and being dependent on special data acquisition methods, such as rotation. An original method that combines a plane compression method with an ellipsoid fitting method is proposed in this paper, which can simultaneously complete the calibration calculation of magnetometer error and misalignment error in one experiment. The calculation can be performed using the spatial scatter point data required by the traditional attitude-independent magnetometer calibration method, and no additional mechanical equipment is required. A mathematical analysis of this method is performed to study the elements decreasing the measurement accuracy of the system, and numerical simulation and field experiments are performed to validate the analysis. The results indicate that the method can contribute to the accuracy improvement of magnetic vector measurement systems.

On the Upmsat-2 Magnetometer's Calibration Methods Performance Comparison for Poorly Conditioned Datasets

On the Upmsat-2 Magnetometer's Calibration Methods Performance Comparison for Poorly Conditioned Datasets

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

In order to improvethe yaw angle accuracy of multi-rotor unmanned aerial vehicle and meet the requirement of autonomous flight, a new calibration and compensation method for magnetometer based on Levenberg–Marquardt algorithm is proposed in this paper. A novel mathematical calibration model with clear physical meaning is established. “Hard iron” error and “Soft iron” error of magnetometer which affect the yaw accuracy of unmanned aerial vehicle are compensated. Initially, Levenberg–Marquardt algorithm is applied to the process of sphere fitting for the original magnetometer data; the optimal estimation of sphere radius and initial “Hard iron” error are obtained. Then, the ellipsoid fitting is performed, and the optimal estimation of “Hard iron” error and “Soft iron” error are obtained. Finally, the calibration parameters are used to compensate for the magnetometer’s output during unmanned aerial vehicle flight. Traditional ellipsoid fitting based on least squares algorithm is taken as reference to prove the effectiveness of the proposed algorithm. Semi-physical simulation experiment proves that the proposed magnetometer calibration method significantly enhances the accuracy of magnetometer. Static test shows that the yaw angle error is reduced from 1.2° to 0.4° when using the proposed calibration model to calibrate magnetometers. In dynamic tests, the sensor MTi’s output is used as reference. The data fusion of magnetometer compensated by the proposed new calibration model based on Levenberg–Marquardt algorithm can accurately track the desired attitude angle. Experimental results indicate that the accuracy of magnetometer in the yaw angle estimation has been greatly enhanced. In the process of attitude estimated, the compensation magnetometer data given by this new method have faster convergence speed, higher accuracy, and better performance than the compensation magnetometer data given by traditional ellipsoid fitting based on least squares algorithm.

Magnetometer Calibration Using Ellipsoid Fitting Method

Considering various applications of magnetic sensors, it is of high importance to correctly calibrate them. Through the conducted experiment here, in order to approach to real conditions and to count for the effect of the environmental magnetic field, the magnetometer has been installed on a vehicle and is moved in a deserted area in Iran within a small radius. The values read by the sensor were then saved in a file and subsequently calibration has been applied using Ellipsoid Fitting Method and MATLAB software. In this paper we have first briefly explained about various magnetometer calibration methods and then the results of the experiments are studied and presented.

A Novel Calibration Method for Magnetometer Array in Nonuniform Background Field

The measurement accuracy of the magnetometer system is closely related to the calibration effect. Available magnetometer calibration methods face the limitation on the assumption that the background magnetic field is uniform. The fact is that the experiment magnetic environment is inhomogeneous for the presence of human activities mainly. This problem could be partially addressed by an auxiliary large-scale triaxial Helmholtz coil to compensate the nonuniformity of magnetic field. This paper proposes a novel calibration method based on the tensor invariants in the nonuniform magnetic field without extra device. The inhomogeneity of background field implies a nonzero gradient field; a new correction model in the gradient field has been established. Calibration parameters are estimated to compensate the observed data using the concept of magnetic gradient tensor and the corresponding rotational tensor invariants, combined with the Levenberg Marquardt optimization. And after the calibration operation, the tensor invariants should be close to a constant. Numerical simulations have been designed and carried out, showing that the proposed algorithm had a good compensation performance with fast convergence, robustness in any gradient field. Field experiments with fluxgate magnetometer array were also conducted to verify this calibration procedure. The measured results are consistent with the simulation results. The root-mean-square (RMS) error is reduced from 616.239 to 1.608 nT/m, after calibration. In addition, the machining error of the frame, the time stability of the background field, the scale of gradient field, and robustness of this proposed method have been discussed.

Use of Magnetic Field for Mitigating Gyroscope Errors for Indoor Pedestrian Positioning.

In the field of indoor pedestrian positioning, the improved Quasi-Static magnetic Field (iQSF) method has been proposed to estimate gyroscope biases in magnetically perturbed environments. However, this method is only effective when a person walks along straight-line paths. For other curved or more complex path patterns, the iQSF method would fail to detect the quasi-static magnetic field. To address this issue, a novel approach is developed for quasi-static magnetic field detection in foot-mounted Inertial Navigation System. The proposed method detects the quasi-static magnetic field using the rate of change in differences between the magnetically derived heading and the heading derived from gyroscope. In addition, to eliminate the distortions caused by system platforms and shoes, a magnetometer calibration method is developed and the calibration is transformed from three-dimensional to two-dimensional coordinate according to the motion model of a pedestrian. The experimental results demonstrate that the proposed method can provide superior performance in suppressing the heading errors with the comparison to iQSF method.

Magnetometer and Gyroscope Calibration Method with Level Rotation.

Micro electro mechanical system (MEMS) gyroscopes and magnetometers are usually integrated into a sensor module or chip and widely used in a variety of applications. In existing integrated gyroscope and magnetometer calibration methods, rotation in all possible orientations is a necessary condition for a good calibration result. However, rotation around two or more axes is difficult to attain, as it is limited by the range of movement of vehicles such as cars, ships, or planes. To solve this problem, this paper proposes an integrated magnetometer and gyroscope calibration method with level rotation. The proposed method presents a redefined magnetometer output model using level attitude. New gyroscope and magnetometer calibration models are then deduced. In addition, a simplified cubature Kalman filter (CKF) is established to estimate calibration parameters. This method possesses important value for application in actual systems, as it only needs level rotation for real-time calibration of gyroscopes and magnetometers. Theoretical analysis and test results verify the validity and feasibility of this method.

Weighted Least Square-Based Magnetometer Calibration Method Robust in Roll-Pitch Limited Conditions

Weighted Least Square-Based Magnetometer Calibration Method Robust in Roll-Pitch Limited Conditions

Extended Kalman Filter Based Gyroscope Aided Magnetometer Calibration for Consumer Electronic Devices

Magnetometers are low-cost sensors that are widely used for orientation sensing in consumer electronic devices. The sensor is affected by non-idealities that can be greatly compensated by proper calibration and determining parameters, such as bias, non-orthogonality, gain, and hard/soft iron. This paper presents a magnetometer calibration method, with the aid of a gyroscope. The algorithm is designed on a principle that the variation of magnetometer output should be aligned with device rotation, which can be sensed by the gyroscope. Based on this notion, an extended Kalman filter is established to estimate calibration parameters. The method evaluation is achieved by first running numerous simulations, followed by a series of experiments on real devices. The result shows that the proposed method can achieve within ten degrees of accuracy, with less than 5 s of convergence. Compared with least square ellipsoid fitting, our proposed method delivers five times better accuracy and avoids over fitting on ill-distributed calibration data sets. This advantage is especially useful for autonomous dynamic calibration in consumer electronic devices.

On Calibration of Three-Axis Magnetometer

Magnetometer has received wide applications in attitude determination and scientific measurements. Calibration is an important step for any practical magnetometer use. The most popular three-axis magnetometer calibration methods are attitude-independent and have been founded on an approximate maximum likelihood (ML) estimation with a quartic subjective function, derived from the fact that the magnitude of the calibrated measurements should be constant in a homogeneous magnetic field. This paper highlights the shortcomings of those popular methods and proposes to use the quadratic optimal ML estimation instead for magnetometer calibration. Simulation and test results show that the optimal ML calibration is superior to the approximate ML methods for magnetometer calibration in both accuracy and stability, especially for those situations without sufficient attitude excitation. The significant benefits deserve the moderately increased computation burden. The main conclusion obtained in the context of magnetometer in this paper is potentially applicable to various kinds of three-axis sensors.

Micromagnetometer calibration for accurate orientation estimation.

Micromagnetometers, together with inertial sensors, are widely used for attitude estimation for a wide variety of applications. However, appropriate sensor calibration, which is essential to the accuracy of attitude reconstruction, must be performed in advance. Thus far, many different magnetometer calibration methods have been proposed to compensate for errors such as scale, offset, and nonorthogonality. They have also been used for obviate magnetic errors due to soft and hard iron. However, in order to combine the magnetometer with inertial sensor for attitude reconstruction, alignment difference between the magnetometer and the axes of the inertial sensor must be determined as well. This paper proposes a practical means of sensor error correction by simultaneous consideration of sensor errors, magnetic errors, and alignment difference. We take the summation of the offset and hard iron error as the combined bias and then amalgamate the alignment difference and all the other errors as a transformation matrix. A two-step approach is presented to determine the combined bias and transformation matrix separately. In the first step, the combined bias is determined by finding an optimal ellipsoid that can best fit the sensor readings. In the second step, the intrinsic relationships of the raw sensor readings are explored to estimate the transformation matrix as a homogeneous linear least-squares problem. Singular value decomposition is then applied to estimate both the transformation matrix and magnetic vector. The proposed method is then applied to calibrate our sensor node. Although there is no ground truth for the combined bias and transformation matrix for our node, the consistency of calibration results among different trials and less than 3(°) root mean square error for orientation estimation have been achieved, which illustrates the effectiveness of the proposed sensor calibration method for practical applications.

Calibration of SQUID vector magnetometers in full tensor gradiometry systems

Measurement of magnetic vector or tensor quantities, namely of field or field gradient, delivers more details of the underlying geological setting in geomagnetic prospection than a scalar measurement of a single component or of the scalar total magnetic intensity. Currently, highest measurement resolutions are achievable with superconducting quantum interference device (SQUID)-based systems.Due to technological limitations, it is necessary to suppress the parasitic magnetic field response from the SQUID gradiometer signals, which are a superposition of one tensor component and all three orthogonal magnetic field components. This in turn requires an accurate estimation of the local magnetic field. Such a measurement can itself be achieved via three additional orthogonal SQUID reference magnetometers. It is the calibration of such a SQUID reference vector magnetometer system that is the subject of this paper.A number of vector magnetometer calibration methods are described in the literature. We present two methods that we have implemented and compared, for their suitability of rapid data processing and integration into a full tensor magnetic gradiometry, SQUID-based, system.We conclude that the calibration routines must necessarily model fabrication misalignments, field offset and scale factors, and include comparison with a reference magnetic field. In order to enable fast processing on site, the software must be able to function as a stand-alone toolbox.

Hysteresis effects in the slope calibration method for magnetometers

The slope method for calibrating vibrating sample magnetometers requires spherical samples of high permeability such that µ + 2/µ - 1 ≈ 1 It is shown that the presence of a small remanence in the calibration spheres results in an effective permeability that is as great if not greater than the initial permeability often used in the above equation. This even higher permeability further increases the validity of this approximation.