• Journal of Institute of Control, Robotics and Systems
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• v.8 no.6
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• pp.524-532
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• 2002

This paper presents a new in-flight alignment method for an SDINS under large initial heading error. To handle large heading error, a new attitude error model is introduced. The attitude errors are divided into heading error and leveling errors using a newly defined horizontal frame. Some navigation error dynamic models are derived from the attitude error model for indirect feedback filtering of the in-flight alignment system. A Kalman filter with Position measurement is designed to estimate navigation errors as the indirect feedback filter Simulation results show that the proposed in-flight alignment method reduces the heading error very quickly from more than 40deg to about 5deg so as to apply a refined navigation filter. The total alignment process including leveling mode and navigation mode in addition to the proposed one allows large initial values not only in heading error but also in leveling errors.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.990-994
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• 2003

When the low cost IMU is used, the result of the stationary self alignment is not suitable for navigation. In this paper, an in-motion alignment method is proposed to obtain an accurate initial attitude of a low cost IMU based GPS/INS integration system. To design Kalman filter for alignment, large heading error model is introduced. And then Kalman filter is designed to estimate initial attitude error as the indirect feedback filter. In order to assess performance of the alignment method, computer simulations are carried out. The simulation results show that initial attitude error rapidly reduces.

• Journal of Advanced Navigation Technology
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• v.28 no.4
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• pp.379-385
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• 2024

INS(inertial navigation system) aligns itself using gravity and Earth's rotational rate from accelerometers and gyro sensors when stationary. Typically, ZUPT(zero velocity update), which is based on a linear error model Kalman filter, is used when it is stationary. However, such algorithms assume stationary conditions, leading to increased alignment errors or filter divergence during maritime mooring due to wave-induced motion. This paper designs a mooring alignment algorithm for maritime platforms using a Kalman filter, which uses large heading angle error model and an initial position correction technique. And it is validated by simulation. Furthermore, it is confirmed that applying this to a rotational INS dramatically improves performance through the principle of bias cancellation.