Hong, J.H. Kang, D. Kim, I.-J. 2024-01-19T15:34:15Z 2024-01-19T15:34:15Z 2021-09-02 2021-01 0018-9456 https://pubs.kist.re.kr/handle/201004/117593 Self-calibration of a magnetometer usually requires controlled magnetic environment as the calibration output can be affected by field distortions from nearby magnetic objects. In this article, we develop a two-stage method that can accurately self-calibrate magnetometer from measurements containing anomalous readings due to local magnetic disturbances. The method proceeds by robustly fitting an ellipsoid to measurement data via L1-norm convex optimization, yielding initial model variables that are less prone to magnetic disruptions. These are then served as a starting point for robust nonlinear least-squares optimization, which refines the magnetometer model to minimize sensor estimation errors while suppressing heavy anomalies. Synthetic and real experimental results are provided to demonstrate improved accuracy of the proposed method in the presence of outliers. We additionally show empirically that the method is directly applicable to self-calibration of three-axis accelerometers. ？ 1963-2012 IEEE. English Institute of Electrical and Electronics Engineers Inc. Robust Autocalibration of Triaxial Magnetometers Article 10.1109/TIM.2020.3035184 1 IEEE Transactions on Instrumentation and Measurement, v.70 IEEE Transactions on Instrumentation and Measurement 70 N scie scopus 000681487400033 2-s2.0-85097944288 Engineering, Electrical & Electronic Instruments & Instrumentation Engineering Instruments & Instrumentation Article Calibration Convex optimization Magnetism 3-axis accelerometer Auto calibration Field distortions Magnetic disturbance Magnetic environments Nonlinear least-squares optimization Sensor estimation Tri-axial magnetometer Magnetometers Accelerometer calibration magnetometer nonlinear least squares robust optimization