Calibration method for misalignment angles of a fiber optic gyroscope in single-axis rotational inertial navigation systems
Author(s) -
Jingxuan Ban,
Gang Chen,
Yue Meng,
Junfeng Shu
Publication year - 2022
Publication title -
optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.394
H-Index - 271
ISSN - 1094-4087
DOI - 10.1364/oe.449629
Subject(s) - gimbal , gyroscope , calibration , sagnac effect , optics , rotation (mathematics) , inertial navigation system , fibre optic gyroscope , ring laser gyroscope , physics , orientation (vector space) , euler angles , optical axis , inertial frame of reference , computer science , interferometry , optical fiber , computer vision , mathematics , classical mechanics , geometry , quantum mechanics , lens (geology)
In a fiber optic gyroscope rotational inertial navigation system (RINS), attitude errors may change after vibration due to the change of misalignment angles. There are two kinds of misalignment angles which can cause the same attitude errors: the one is misalignment angles of gyroscopes, and the other is misalignment angles between input axis of gyroscope and rotating gimbal axis. Thus, it is difficult to calibrate any kind of misalignment angles by attitude errors alone. Self-calibration methods can separate and calibrate the two kinds of misalignment angles. But single-axis RINSs rely on a turntable to realize the rotation scheme. And misalignment angles may change during repeated removal. Therefore, it is necessary to study an efficient and convenient method to analyze which kind of misalignment angles leads to the change of attitude errors and calibrate these misalignment angles. According to the different influences of two kinds of misalignment angles on navigation errors and fine alignment errors, this paper proposes a calibration method based on fine alignment algorithm to calibrate the gyroscopes' misalignment angles. Its accuracy is proven by simulations and experiments. From experimental results, position errors have decreased at least 21.4% with the proposed method.
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