Abstract
This paper presents an unscented Kalman filter (UKF)-based algorithm for orbit determination and sensor calibration. In the design, the magnetometer data are used for the determination of satellite position and velocity. As the orbit dynamics and geomagnetic filed models are nonlinear, the UKF which is capable of addressing nonlinearities is adopted. In addition, the sensor is subject to biases and scale factor errors, it is shown that the UKF-based orbit determination algorithm can be extended to become a two-tiered approach in accounting for orbit determination and sensor calibration. The proposed algorithm is applied to process the ESESM (Experimental Scientific-Education Micro-Satellite) flight data. The capability of the algorithm in autonomous orbit determination and sensor calibration is verified.
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