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 use various nonlinear filtering methods to solve this problem. However, these filtering methods not only require heavy calculation, but also suffer from the low accuracy problem. To solve these problems, 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. Within the MATLAB simulation, pitch, roll and heading errors can be stabilized at about 10 degrees. The real-world experiment with Redmi K30Pro mobile phone shows that the alignment result of the improved algorithm is more accurate. The difference of multiple alignment results is within 5 degrees when IAKF is used, while the difference of traditional filter is more than 20 degrees.