Magnetometer Calibration Using a Nonlinear Optimal Approach

Magnetometer is one of the main sensors in satellite attitude determination and control subsystems and its data calibration plays an important role in mission’s success. In this paper, the subject of magnetometer calibration using two optimal approaches is analyzed and their robustness in the presence of measurement disturbances is investigated. In this regard, firstly, a measurement model which contains main magnetometer parameters, namely, biases, scale factors and non-orthogonality corrections is presented. Then, two approaches for magnetometer calibration are proposed. In the first approach which is a centered method, nonlinear magnetometer calibration problem is transferred to a linear problem and calibration parameters are derived. However, in the second method which is based on maximum likelihood approach, magnetometer calibration problem is considered as a nonlinear problem and calibration parameters are estimated. Two kinds of magnetic field profiles are considered to evaluate the performance of calibration methods for a LEO satellite. According to the results, accuracy of the maximum likelihood approach is much better than centered method. Finally, in order to assess the robustness of the two presented methods, 100 Monte Carlo simulations are performed. Based on the Monte Carlo simulations results, estimation of calibration parameters using maximum likelihood approach is much smoother and calibration parameters are estimated more accurately.