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IEEE transactions on instrumentation and measurement v.66 no.2, 2017년, pp.243 - 253   SCI SCIE
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

A Self-Calibration Method for Accelerometer Nonlinearity Errors in Triaxis Rotational Inertial Navigation System

Gao, Pengyu (School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing, China ); Li, Kui (School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing, China ); Wang, Lei (School of Instrumentation Science and Opto-electronics Engineering, Beihang University, Beijing, China ); Liu, Zengjun ( );
  • 초록  

    The navigation performance of the rotational inertial navigation system (RINS) could be greatly improved by rotating the inertial measurement unit with gimbals, and self-calibration for error parameters could be achieved in RINS as well. However, accelerometer nonlinearity errors need to be considered and calibrated to further improve the navigation accuracy of RINS, especially in large dynamic applications. In this paper, a self-calibration method is proposed for accelerometer nonlinearity errors in triaxis RINS. Accelerometer nonlinearity errors and other errors are calibrated through optimal estimation with velocity and position error measurements. In order to guarantee that all errors are observable during calibration, some rotation scheme design principles are proposed, which are different from traditional observability analysis methods and could provide instructions for rotation scheme design directly. The effectiveness of the self-calibration method is proved by both simulation and experiment. The accelerometer nonlinearity errors could be accurately calibrated with the proposed method, while other error parameters reach higher calibration accuracy. Furthermore, experiment results from a long-term vehicle navigation show that velocity and position accuracy of the triaxis RINS have improved significantly after compensation with the self-calibration results, fully illustrating the significance of the proposed self-calibration method in improving the navigation performance of RINS.


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