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http://dx.doi.org/10.5574/KSOE.2017.31.2.177

Bias Estimation of Magnetic Field Measurement by AHRS Using UKF  

Ko, Nak Yong (Department Electronic Engineering, Chosun University)
Song, Gyeongsub (Department Electronic Engineering, Chosun University)
Jeong, Seokki (Department Electronic Engineering, Chosun University)
Lee, Jong-Moo (Korea Research Institute of Ships & Ocean Engineering)
Choi, Hyun-Taek (Korea Research Institute of Ships & Ocean Engineering)
Moon, Yong Seon (Department Electronic Engineering, Sunchon National University)
Publication Information
Journal of Ocean Engineering and Technology / v.31, no.2, 2017 , pp. 177-182 More about this Journal
Abstract
This paper describes an unscented Kalman filter approach to estimate the bias in magnetic field measurements. A microelectromechanical systems attitude heading reference system (MEMS AHRS) was used to measure the magnetic field, together with the acceleration and angular rate. A magnetic field is usually used for yaw detection, while the acceleration serves to detect the roll and pitch. Magnetic field measurements are vulnerable to distortion due to hard-iron effect and soft-iron effect. The bias in the measurement accounts for the hard-iron effect, and this paper focuses on an approach to estimate this bias. The proposed method is compared with other methods through experiments that implement the navigation of an underwater robot using an AHRS and Doppler velocity log. The results verify that the compensation of the bias by the proposed method improves the navigation performance more than or comparable to the compensation by other methods.
Keywords
AHRS; Unscented Kalman filter; Bias; Estimation; Underwater;
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  • Reference
1 Hashmall, J., Deutschmann, J., 1996. An Evaluation of Attitude-Independent Magnetometer-Bias Determination Methods. Flight Mechanics/Estimation Theory Symposium, 169-178.
2 Kim, E., Bang, H., Yong, K.L., Lee, S.H., 2006. Three-axis Magnetometer Bias Estimation. The Korean Society For Aeronautical And Space Sciences, 818-821.
3 Ko, N.Y., Choi, H.T., Lee, C.M., 2016a. Navigation of Unmanned Surface Vehicle and Detection of GPS Abnormality by Fusing Multiple Sensor Measurements. OCEANS 2016 MTS/IEEE Monterey, California USA, 19-23.
4 Ko, N.Y., Choi, H.T., Lee, C.M., Moon, Y.S., 2016b. Attitude Estimation Using Depth Measurement and AHRS for Underwater Vehicle Navigation. OCEANS 2016 MTS/IEEE Shanghai, China, 10-13.
5 Ko, N.Y., Jeong, S., 2016, Fused Navigation of Unmanned Surface Vehicle and Detection of GPS Abnormality. Institute of Control, Robotics and Systems, 22(9), 723-732.   DOI
6 Ko, N.Y., Jeong, S., Bae, Y., 2016c. Sine Rotation Vector Method for Attitude Estimation of an Underwater Robot. Sensors, 16(8).
7 Ko, N.Y., Kim, T.G., Choi, H.T., 2015. Synchronous and Asynchronous Application of a Filtering Method for Underwater Robot Localization. International Journal of Humanoid Robotics, 13(2).
8 Ko, N.Y., Kuc, T.Y., 2015. Fusing Range Measurements from Ultrasonic Beacons and a Laser Range Finder for Localization of a Mobile Robot. Sensors, 15(5), 11050-11075.   DOI
9 National Oceeanic and Atmospheric Administration (NOAA), 2016. Magnetic Field Calculators. [Online] Available at: [Accessed 2016].
10 Rhudy, M.T., Gu, Y., 2013. Understanding Nonlinear Kalman Filters, Part II: An Implementation Guide. Interactive Robotics Letters(IRL), West Virginia University, [Online] Available at: [Accessed 2016].
11 Thrun, S., Burgard, W., Fox, D., 2006. Probabilistic Robotics. The MIT Press, Massachusetts, 220-223.
12 Troni, G.C., Whitcomb, L., 2013. Adaptive Estimation of Measurement Bias in Three-Dimensional Field Sensors with Angular-Rate Sensors: Theory and Comparative Experimental Evaluation. Robotics: Science and Systems.