On the UPMSat-2 magnetometer's calibration methods performance comparison for poorly conditioned datasets. (15th February 2023)

• Record Type: Journal Article
• Authors: Rodríguez-Rojo, Elena; Cubas, Javier; Pindado, Santiago
• Is Part Of: Measurement. Volume 207(2023)
• Publication Date: 2023-02-15
• Subjects: Space Systems Engineering -- Attitude Determination and Control Subsystem -- Magnetometer calibration -- Geometric fitting algorithms -- Least Squares Algorithms -- Poorly conditioned data sets practices for fitting algorithms; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2022.112381
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Methodology robust to ill-conditioned data sets for magnetometers calibration. Testing of fitting methods for an ellipsoid defined by a noisy data set. Testing of fitting methods for non-homogeneous and sparse ellipsoidal data sets. Abstract: This article summarises the work related to the calibration methodology of the Attitude, Control and Determination Subsystem (ADCS) sensors used in the UPMSat-2 mission project, a 50 kg satellite designed and manufactured by the Universidad Politécnica de Madrid . The ADCS sensors are two fluxgate magnetometers whose calibration is essential to obtain correct measurements. Since the calibration process must be performed once the magnetometers are integrated in the satellite, the range of movements to obtain a representative set of measurements distributed in space is limited. n the present work, several geometric adjustment algorithms are evaluated in order to find the best mathematical structure for this particular problem: underconditioned measurements in the calibration scenarios. The magnetometer data obtained in the satellite integration test are used, together with the selected fitting algorithm, to obtain the calibration parameters of the sensors.