Calibration of Low-Cost Three Axis Magnetometer with Differential Evolution
The magnetometers are used in wide range of engineering applications. However, the accuracy of magnetometer readings is influenced by many factors such as sensor errors (scale factors, non-orthogonality, and offsets), and magnetic deviations (soft-iron and hard-iron interference); therefore, the magnetic calibration of magnetometer is necessary before its use in specific applications. This research paper describes calibration method for three axis low-cost MEMS (Micro-Electro-Mechanical Systems) magnetometer. The calibration method uses differential evolution (DE) algorithm for the determination of the transformation matrix (scale factor, misalignment error, and soft iron interference) and bias offset (hard-iron interference). The performance of this method is analysed in experiment on three axis low-cost magnetometer LSM303DLHC and then compared to the traditional method (least square ellipsoid fitting method). The magnetometer readings were obtained while rotating the sensor around arbitrary rotations. The experimental results show that the calibration error is least using DE.
KeywordsCalibration Differential Evolution Magnetometer MEMS
This work was supported by Internal Grant Agency of Tomas Bata University in Zlin under the project No. IGA/FAI/2017/007.
- 2.Haverinen, J., Kemppainen, A.: A geomagnetic field based positioning technique for underground mines. In: 2011 IEEE International Symposium on Robotic and Sensors Environments (ROSE), pp. 7–12. IEEE, September 2011Google Scholar
- 3.Ashkar, R., Romanovas, M., Goridko, V., Schwaab, M., Traechtler, M., Manoli, Y.: A low-cost shoe-mounted inertial navigation system with magnetic disturbance compensation. In: 2013 International Conference on Indoor Positioning and Indoor Navigation, IPIN 2013 (2013)Google Scholar
- 5.Glanzer, G., Walder, U.: Self-contained indoor pedestrian navigation by means of human motion analysis and magnetic field mapping. In: Proceedings of the 2010 7th Workshop on Positioning, Navigation and Communication, WPNC 2010, pp. 303–307 (2010)Google Scholar
- 6.Guo, P., Qiu, H., Yang, Y., Ren, Z.: The soft iron and hard iron calibration method using extended kalman filter for attitude and heading reference system. In: 2008 IEEE/ION Position, Location and Navigation Symposium, pp. 1167–1174. IEEE (2008)Google Scholar
- 8.Kok, M., Hol, J., Schon, T., Gustafsson, F., Luinge, H.: Calibration of a magnetometer in combination with inertial sensors. In: 2012 15th International Conference on Information Fusion (FUSION), pp. 787–793 (2012)Google Scholar
- 12.Cheuk, C.M., Lau, T.K., Lin, K.W., Liu, Y.: Automatic calibration for inertial measurement unit. In: 2012 12th International Conference on Control Automation Robotics and Vision (ICARCV), pp. 1341–1346. IEEE, December 2012Google Scholar
- 13.Sarcevic, P., Pletl, S., Kincses, Z.: Evolutionary algorithm based 9DOF sensor board calibration. In: 2014 IEEE 12th International Symposium on Intelligent Systems and Informatics (SISY), pp. 187–192. IEEE, September 2014Google Scholar
- 14.Storn, R., Price, K.: Differential evolution - a simple and efficient adaptive scheme for global optimization over continuous spaces, Technical report (1995)Google Scholar
- 15.Storn, R.: On the usage of differential evolution for function optimization. In: Proceedings of North American Fuzzy Information Processing, pp. 519–523. IEEE (1996)Google Scholar
- 16.STMicroelectronics, Data brief: STEVAL-MKI124V1, p. 4 (2013)Google Scholar
- 17.National Centers of Environmental Information (2016)Google Scholar