Transfer Alignment Method and Realization of SINS on Moving Base Based on Kalman Filter

Cong, Mingyu; Lü, Huan Huan; Cheng, Xianghong; Liang, Hao

doi:10.1109/iccasit48058.2019.8973175

Cited by 4 publications

(1 citation statement)

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“…At present, there are many methods used in the fine alignment stage. The most commonly used method is the Kalman filter method based on the "prediction + correction" idea [7] [8]. The error equation is established based on the dynamic model to determine the attitude parameters, and related error sources are estimated and compensated in real time.…”

Section: Introductionmentioning

confidence: 99%

Improved initial alignment algorithm of SINS on shaking base based on Kalman filter

Shi

Hou

Shi

et al. 2022

2022 Sensor Data Fusion: Trends, Solutions, Applications (SDF)

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In the initial alignment of shaking base, Kalman filter is easy to diverge, and the alignment result is poor. Many scholars introduce various nonlinear filtering methods to solve this problem, but these filtering methods not only have large amount of calculation, but also are low accuracy. Based on the problem, an improved adaptive Kalman filter (IAKF) algorithm is proposed to complete the initial alignment of the shaking base. The experimental results show that the improved algorithm has high anti-interference and anti-divergence ability. Under the simulation conditions, pitch, roll and heading errors can be stabilized at about 10 degrees. The real measurement with Redmi K30Pro mobile phone shows that the alignment result of the improved algorithm is consistent, and the difference of multiple alignment results is stable within 5 degrees, while the difference of traditional method is more than 20 degrees. It can be applied in smart phone navigation with the strapdown inertial navigation system.

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Section: Introductionmentioning

confidence: 99%