Magnetometer Error Research Articles

Integrated Compensation of Magnetometer Array Magnetic Distortion Field and Improvement of Magnetic Object Localization

A fluxgate magnetometer array for magnetic object localization is designed, where hard-iron and soft-iron magnetic distortion fields are the major factors influencing measurement accuracy. A vector compensation method is proposed to suppress error, in which magnetometer error, misalignment error, and magnetic distortion fields are considered. The experimental system mainly consists of a plane cross magnetometer array, a magnet (to be hard-iron), a steel block (to be soft-iron), and a deployment platform (to change the attitude of the magnetometer array). Experimental results show that integrated compensation parameters can be obtained accurately, and array difference errors are reduced about two orders, thus proving the effectiveness of the vector compensation method. The compensated array is used for static and dynamic localization in 3-D. In static situation, localization errors are reduced from 0.17 m, 0.28 m, and 0.27 m to 0.03 m, 0.05 m, and 0.14 m, respectively. On the object deployment trace, error intensity is reduced from 0.17 to 0.04 m. In particular, the dynamic localization results are unreliable without compensation, and the error intensity is reduced from 2.47 to 0.05 m using the proposed method, thus improving the localization accuracy.

Novel calibration algorithm for a three-axis strapdown magnetometer.

A complete error calibration model with 12 independent parameters is established by analyzing the three-axis magnetometer error mechanism. The said model conforms to an ellipsoid restriction, the parameters of the ellipsoid equation are estimated, and the ellipsoid coefficient matrix is derived. However, the calibration matrix cannot be determined completely, as there are fewer ellipsoid parameters than calibration model parameters. Mathematically, the calibration matrix derived from the ellipsoid coefficient matrix by a different matrix decomposition method is not unique, and there exists an unknown rotation matrix R between them. This paper puts forward a constant intersection angle method (angles between the geomagnetic field and gravitational field are fixed) to estimate R. The Tikhonov method is adopted to solve the problem that rounding errors or other errors may seriously affect the calculation results of R when the condition number of the matrix is very large. The geomagnetic field vector and heading error are further corrected by R. The constant intersection angle method is convenient and practical, as it is free from any additional calibration procedure or coordinate transformation. In addition, the simulation experiment indicates that the heading error declines from ±1° calibrated by classical ellipsoid fitting to ±0.2° calibrated by a constant intersection angle method, and the signal-to-noise ratio is 50 dB. The actual experiment exhibits that the heading error is further corrected from ±0.8° calibrated by the classical ellipsoid fitting to ±0.3° calibrated by a constant intersection angle method.

Error Compensation of 3-Axis Magnetoresistive Magnetometer

To eliminate errors of 3-axis magnetoresistive magnetometer, according to its offset errors, sensitivity errors and non-orthogonal errors which specialty was considered, an error compensation model of magnetoresistive magnetometer was established. And the calculation method of the error compensation model, which did not need accelerometers, was proposed to correct the magnetometer errors. The results show the algorithm of error compensation model decrease the root mean square error of the magnetometer from 413nT to 82nT. Thus the 3-axis magnetoresistive magnetometer could fast and accurately measure the geomagnetic field.

A new magnetic compass calibration algorithm using neural networks

The magnetic compass can provide heading direction by measuring the Earth's magnetic field. In practical applications, there usually exists an unwanted local magnetic field that will distort the magnetometer measurements; hence a calibration procedure is essential. Current calibration methods are limited by the inaccurate magnetometer error estimation when measurements are deteriorated by magnetic disturbances or large noises. This paper proposes a new compass calibration algorithm via modelling the nonlinear relationship between the compass heading and the true heading using neural networks. When an external heading reference is available, neural networks can be trained to properly model this nonlinear input–output pattern even in the presence of magnetic disturbances, and subsequently can be applied to convert the compass heading into the correct heading. The proposed algorithm does not require declination information and magnetometer biases and scale factor estimation. The simulation and field test results have verified the effectiveness and robustness of the proposed calibration method and have also shown that the calibration performance is proportional to the quality of the training data.