Improved Initial Alignment Algorithm of SINS on Shaking Base Based on Kalman Filter

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. In theory, standard Kalman filtering can obtain the optimal estimate of the state under the condition that the structural parameters and noise statistical characteristic parameters of the random system are accurately known. Based on the problem, an improved adaptive Kalman filter (IAKF) algorithm is proposed to complete the initial alignment of the shaking base. While performing state estimation, it can also estimate the system's noise parameters in real time through measurement output. 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 and iPhone 14 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.