The Study of Kalman Filtering Algorithm in the Initial Alignment of Strapdown Inertial Navigation System

2015 ◽  
Vol 740 ◽  
pp. 596-599 ◽  
Author(s):  
Shi Qi An ◽  
Jun Kai Zhang

According to the principle and the method of initial alignment of strapdown inertial navigation system, proposed based on Sage-Husa adaptive kalman filter algorithm. The measured simulation data, compared with those of kalman filtering algorithm, show that the optimized algorithm can optimize the noise estimation, revise accumulated error of strapdown inertial navigation system, and greatly improve the navigation accuracy.

Author(s):  
Hossein Rahimi ◽  
Amir Ali Nikkhah ◽  
Kaveh Hooshmandi

This study has presented an efficient adaptive unscented Kalman filter (AUKF) with the new measurement model for the strapdown inertial navigation system (SINS) to improve the initial alignment under the marine mooring conditions. Conventional methods of the accurate alignment in the ship’s SINS usually fail to succeed within an acceptable period of time due to the components of external perturbations caused by the movement of sea waves and wind waves. To speed up convergence, AUKF takes into account the impact of the dynamic acceleration on the filter and its gain adaptively tuned by considering the dynamic scale sensed by accelerometers. This approach considerably improved the corrections of the current residual error on the SINS and decreased the influence due to the external perturbations caused by the ship’s movement. Initial alignment algorithm based on AUKF is designed for large misalignment angles and verified by experimental data. The experimental test results show that the proposed algorithm enhanced the convergence speed of SINS initial alignment compared with some state-of-the-art existing approaches.


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