In-Motion Coarse Alignment Based on the Vector Observation for SINS

In this paper, an in-motion coarse alignment based on the optimization-based method and vector observation for strapdown inertial navigation system is proposed. The traditional coarse alignment method converges slowly on the moving base, owing to the measurement noises of the observation measurements. To solve this problem, the method of sliding fixed-interval integration is utilized to construct the observation vector model, which can suppress the noises in the measurements. An optimization-based method named optimal-REQUEST algorithm is used to determine the attitude quaternion, which can further filter the measurement noise by adjusting the gain of the filter adaptively. To verify the performance of the proposed method, an in-motion simulation and vehicle tests are carried out. The results show that the convergence rate of the proposed method is faster than that of the compared methods, and the convergence process of the first one is more stable than that of the others.