Particle swarm optimization for magnetometer calibration with rotation axis fitting using in-orbit data
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
IEEE Transactions on Aerospace and Electronic Systems, Volume 58, issue 2
AbstractThis article demonstrates the performance of an improved particle swarm optimization (PSO) algorithm with scalar checking and rotation axis fitting objectives using in-orbit data, which is obtained from two CubeSats missions, Aalto-1 and ESTCube-1, as well as simulation as reference. The improved algorithm uses sequential objectives refinement process to combine the two optimization objectives. This improvement addresses some challenges of magnetometer calibration when using in-orbit data. First, the change in the magnetic field vector direction at different points in orbit which is uncorrelated to the rotation of the spacecraft itself. Second, the uncertainty of the rotation axis information used as the reference, e.g., from gyroscope noise. Third, the available data set is heavily affected by the rotation mode of the spacecraft, which imposes some limitation in the rotation axis information needed by the algorithm. The improved PSO algorithm is applied on simulated data in order to analyze the calibration performance under different spacecraft tumbling rates and noise levels. In ideal condition (varying rotation axis during measurements and sufficient sampling rate relative to the spin rate), the rotation axis fitting objective can reach ∼0.1° of correction accuracy.
Calibration, calibration, Gyroscopes, Magnetic separation, Magnetometer, Magnetometers, Mathematical models, nanosatellite, particle swarm optimization, rotation axis fitting, Rotation measurement, Space vehicles
Riwanto , B A , Niemela , P , Ehrpais , H , Slavinskis , A , Mughal , M R & Praks , J 2022 , ' Particle swarm optimization for magnetometer calibration with rotation axis fitting using in-orbit data ' , IEEE Transactions on Aerospace and Electronic Systems , vol. 58 , no. 2 , pp. 1211-1223 . https://doi.org/10.1109/TAES.2021.3122514