This paper aims to present a multiple-model adaptive estimator (MMAE) to calibrate the star sensor low frequency error (LFE). The star sensor LFE, which is caused primarily by the periodic thermal distortion, has a great impact on spacecraft attitude determination accuracy.
The unfavorable effect of the LFE can be partly eliminated by using the calibration algorithm based on the augmented Kalman filter (AKF). However, the AKF may be worse than the traditional Kalman filter (KF) in the absence of the LFE. To cope with this problem, the MMAE is applied first time for combining the AKF and the KF in the spacecraft attitude determination system, such that satisfactory performance can be achieved in different operating scenarios.
The convergence of the presented MMAE is demonstrated through a formal derivation. A novel method is proposed to tune the MMAE design parameter, such that the convergence rate of the estimator is increased. It is shown via numerical studies that the presented algorithm outperforms the AKF and the KF.
The calibration algorithm is applicable for spacecraft attitude determination.
An effective star sensor LFE calibration algorithm based on the MMAE is developed. In addition, a novel method is proposed to increase convergence rate of the estimator.
This study was supported in part by China Natural Science Foundation (61573059), Beijing Natural Science Foundation (4162070) and National 973 Program (2013CB733100).
Xiong, K. and Wei, C. (2017), "Multiple-model adaptive estimator for spacecraft attitude sensor calibration", Aircraft Engineering and Aerospace Technology, Vol. 89 No. 3, pp. 457-467. https://doi.org/10.1108/AEAT-02-2015-0029
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