Abstract
One of the critical challenges in spacecraft attitude estimation is the handling nonlinear non-Gaussian noisy conditions originating from measurement errors, model drifts, and incorrect models. In this article, we propose a new, adaptive and robust nonlinear Kalman filter to address this issue. The proposed filter is derived by casting the problem as a nonlinear regression problem and then realized by building on the robust filter, based on multiple strong tracking with multiple fading factors. In particular, the optimal filter gain is achieved by minimizing the constrained cost function that combines the minimum mean square error and the norm constraint. The resulting algorithm, referred to as the adaptive Huber filter based on the multiple strong tracking (AHFMST), shows promising results. Several simulation scenarios considering the realistic initial errors for a low-orbiting spacecraft attitude estimation are studied to compare the performance of the new algorithm and the state-of-the-art algorithms. The proposed algorithm has demonstrated better performance in terms of robustness and estimation accuracy.
Published Version
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