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Magnetic sensor array is usually designed for object detection and localization, where sensor coordinate is required to be consistent. Misalignment angles between coordinates should be calculated and calibrated. A new calibration method is proposed in order to calibrate the misalignment error between host frame and sensors. Host platform frame and perpendicular platform are used to obtain misalignment angles of sensor array. Experiment results show that it can estimate all angles of magnetic sensors and suppress misalignment error. Misalignment rms error is a reduction from 1109.7 to 27.6 nT, which means 2.48% indeed, thus improving the remote sensing accuracy of array which is unified to host frame coordinate.

Technological limitations in sensor manufacturing and unwanted magnetic fields will corrupt the measurements of three-axis magnetometers. An experiment with four different magnetic disturbance situations is designed, and the influence of hard-iron and soft-iron material is analyzed. The calibration method with magnetic disturbance parameters is proposed for calibration and magnetic disturbance compensation of three axis magnetometers. It is not necessary to compute pseudo-linear parameters, thus the integrated parameters are computed directly by solving nonlinear equations. To employ this method, a nonmagnetic rotation equipment, a CZM-3 proton magnetometer, a DM-050 three-axis magnetometer, two magnets and two steel tubes are used. Calibration performance is discussed in the four situations. Compared with several traditional calibration methods, experiment results show that the proposed method has better integrated compensation performance in all situations, and error is reduced by several orders of magnitude. After compensation, RMS error is reduced from 10797.962 nT to 15.309 nT when the big magnet and steel tube are deployed. It suggests an useful method for calibration and magnetic disturbance compensation of three-axis magnetometers.

Ferromagnetic interferential field from platforms is one of the most dominating error sources for magnetometer. For magnetic vector and gradient tensor measurement, what is cared about most is the effect of compensating magnetic field vector. In this paper, a magnetic compensation method is proposed, which uses host platform’s attitude from inertial sensor as auxiliary information and sets up a vectorial compensation model. By introducing three intermediate parameters, the issue of parameter estimation is linearized and solved with least squares method. Simulations show that errors of magnetic field vector and magnitude can both be reduced to several nT after compensation. Experiment has been conducted with a geomagnetic vector measurement system and results suggest that the method is an effective way for compensating both magnetic field magnitude and vectors.

Geomagnetic field is widely used for remote sensing, environment observation, geoscience and geology research. A portable geomagnetic field vector measurement system is designed, but misalignment greatly influences measurement accuracy. It is calibrated by establishing interim coordinate without measuring the projection of geomagnetic field and gravity. In addition, earth's magnetic dip, the accurate attitude of equipment and careful operation in collection process are not need. It is easy to implement the experiment, and misalignment angles between sensors are calculated. Magnetometer, inertial navigation system, host platform frame and perpendicular platform are used in experiment. After calibration, North, Vertical and East vector measurement error are reduced to 3.9%, 4.7% and 6.6% respectively. Inclination and declination error are reduced from 4.374° and 4.974° to 0.173° and 0.309° respectively. Thus, it is convenient and effective for misalignment calibration.

Magnetic interference is one key factor reducing the vector measurement accuracy of geomagnetic field measurement instrument, where scalar suppression algorithm is ineffective. A convenient component suppression method is proposed to avoid complex equipments and strict operations in previous component methods. The key factor is to overcome the problem that additive unknown parameters are added when attitude changes. System attitude change information is proposed for component suppression, where the attitude change is provided by inertial navigation system (INS). After suppression, magnetic interference of north, vertical and east direction are suppressed to 2.1%, 4.28% and 2.53% respectively. It provides a new and convenient way to improve the accuracy of geomagnetic field vector measurement.

Calibration strategy will influence the calibration performance of three-axis magnetometers. In this paper, three different calibration strategies (symmetrical calibration, orthogonal calibration and random calibration) are introduced and verified by simulation and experiments. The experimental system mainly consists of three-axis fluxgate magnetometer (MAG3300), 2D nonmagnetic rotation equipment and proton magnetometer. The scalar value of magnetic field was obtained with proton magnetometer and considered to be the true value. Calibration performance of three different calibration strategies were analyzed and compared. Experimental results show that after calibration the scalar RMS error has dropped 88.5%, 85.5% and 87.6% respectively. To check the generality of the estimated error parameters, another site was chosen to be a validation position, and the calibration performance of three calibration strategies were tested and compared. After calibration, the scalar RMS error has dropped 83.2%, 78.9% and 78.8% respectively. Thus the symmetrical calibration is considered to be the best calibration strategy among three calibration strategies.

The accuracy of three-axis magnetometers is limited by different scales, bias of each axis and nonorthogonality between axes, which is usually lower than that of scalar magnetometers. In this paper, the nonlinear least square method is proposed to calibrate three-axis magnetometers. The validity of this method was proved by simulation, in which the estimated parameters of the error model are close to prearranged parameters. In experiment, a three-axis fluxgate magnetometer (DM-050), a two dimensional nonmagnetic rotation equipment and a proton magnetometer (GSM-19T) were used. The scalar value of magnetic field was obtained by proton magnetometer and considered to be the true value. The calibration performance of unscented Kalman filter (UKF), two-step algorithm and nonlinear least square were compared. Experimental results show that the error average and standard deviation of nonlinear least square are the least among the three methods. After calibration, the average of scalar error is reduced from −76.2 nT to −0.00093 nT and the standard deviation is reduced from 10.832 nT to 4.298 nT. The results suggest an effective way for the calibration of three-axis fluxgate magnetometers.

The magnetometer array is effective for magnetic object detection and localization. Calibration is important to improve the accuracy of the magnetometer array. A magnetic sensor array built with four three-axis DM-050 fluxgate magnetometers is designed, which is connected by a cross aluminum frame. In order to improve the accuracy of the magnetometer array, a calibration process is presented. The calibration process includes magnetometer calibration, coordinate transformation and misalignment calibration. The calibration system consists of a magnetic sensor array, a GSM-19T proton magnetometer, a two-dimensional nonmagnetic rotation platform, a 12 V-dc portable power device and two portable computers. After magnetometer calibration, the RMS error has been decreased from an original value of 125.559 nT to a final value of 1.711 nT (a factor of 74). After alignment, the RMS error of misalignment has been decreased from 1322.3 to 6.0 nT (a factor of 220). Then, the calibrated array deployed on the nonmagnetic rotation platform is used for ferromagnetic object localization. Experimental results show that the estimated errors of X, Y and Z axes are −0.049 m, 0.008 m and 0.025 m, respectively. Thus, the magnetometer array is effective for magnetic object detection and localization in three dimensions.

Fluxgate magnetometers are widely used for magnetic field measurement. However, their accuracy is influenced by temperature. In this paper, a new method was proposed to compensate the temperature drift of fluxgate magnetometers, in which a least-squares support vector machine (LSSVM) is utilized. The compensation performance was analyzed by simulation, which shows that the LSSVM has better performance and less training time than backpropagation and radical basis function neural networks. The temperature characteristics of a DM fluxgate magnetometer were measured with a temperature experiment box. Forty-five measured data under different magnetic fields and temperatures were obtained and divided into 36 training data and nine test data. The training data were used to obtain the parameters of the LSSVM model, and the compensation performance of the LSSVM model was verified by the test data. Experimental results show that the temperature drift of magnetometer is reduced from 109.3 to 3.3 nT after compensation, which suggests that this compensation method is effective for the accuracy improvement of fluxgate magnetometers.