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Nanosatellite Attitude Estimation and In-Orbit Sensor Calibration Using Only Kinematic Relations

Abstract. In this study, the attitude angles of a nanosatellite are estimated using vector measurements obtained from onboard magnetometers and sun sensors. A nontraditional filtering approach is adopted, relying solely on a kinematic model that is propagated using rate gyroscope and magnetometer data. The estimation process consists of two main stages. In the first stage, attitude measurements are determined using a singular value decomposition (SVD)-based single-frame method. In the second stage, an Extended Kalman Filter (EKF) is implemented, incorporating a linearized attitude model and nonlinear magnetometer measurements. These two stages are integrated into a unified estimation algorithm—referred to as the SVD-Aided EKF—which enables the simultaneous estimation of attitude angles, as well as gyroscope and magnetometer biases, with high accuracy. The rate gyroscope and magnetometer measurements are corrected at each estimation step by subtracting the bias estimates from the corresponding sensor measurements. The bias-free gyroscope measurements are then fed back into the kinematic model, and the bias-free magnetometer measurements are fed back into the SVD algorithm and the measurement update part of Kalman filter, which are also used in the SVD-Aided EKF.