2019
DOI: 10.1109/access.2018.2886598
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The Asynchronous Gimbal-Rotation-Based Calibration Method for Lever-Arm Errors of Two Rotational Inertial Navigation Systems

Abstract: A rotational inertial navigation system (RINS) can enhance the navigation accuracy by modulating the constant inertial device biases with gimbals rotation. To further improve the navigation performance in marine applications, two or more RINSs are usually equipped in the same vessel to achieve the information fusion. However, the lever-arm effect caused by the outer and inner lever-arm errors of two RINSs would decrease the fusion accuracy of velocity and position information. To solve this problem, the asynch… Show more

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Cited by 8 publications
(1 citation statement)
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“…By measuring the difference between the velocity information of GPS and RINS, the Kalman filter model is established to estimate the platform angle and inertial device errors of RINS, and then to conduct the in-motion alignment. In addition, while the vehicle is moving, due to the influence of the lever arm [13][14][15][16] and the asynchronous time [17][18][19] between the INS and GPS, the quality of measurement information is impaired, resulting in observation errors. This then causes a deviation of the estimation of the platform and gyroscope installation angle, which limits the in-motion alignment accuracy.…”
Section: Introductionmentioning
confidence: 99%
“…By measuring the difference between the velocity information of GPS and RINS, the Kalman filter model is established to estimate the platform angle and inertial device errors of RINS, and then to conduct the in-motion alignment. In addition, while the vehicle is moving, due to the influence of the lever arm [13][14][15][16] and the asynchronous time [17][18][19] between the INS and GPS, the quality of measurement information is impaired, resulting in observation errors. This then causes a deviation of the estimation of the platform and gyroscope installation angle, which limits the in-motion alignment accuracy.…”
Section: Introductionmentioning
confidence: 99%