A Coarse Alignment Algorithm Based on Vector Reconstruction via Sage-Husa AKF for SINS on a Swaying Base

As a rapid self-alignment method, the coarse alignment technique, under swaying-base conditions, can enhance initial attitude determination speed without external aids, which is critical for strapdown inertial navigation systems (SINSs). The inaccuracy of the observation vector model caused by inertial sensor error accumulation and external disturbances is a critical constraint on the performance of coarse alignment methods. To address the above issues, a coarse alignment algorithm based on vector reconstruction via the Sage-Husa adaptive Kalman filter (AKF) is proposed in this paper. First, an apparent velocity vector observation model was established. Second, a sliding-window vector integration algorithm was designed to process this observation model, aiming to reduce the cumulative error of the observation vector. Next, a vector reconstruction model based on the Sage-Husa AKF algorithm was designed, and the self-alignment process was completed using the reconstructed observation vector. Finally, simulations and turntable experiments were conducted to demonstrate the effectiveness of the method. The results indicate that this method exhibits alignment performance superior to that of similar coarse alignment methods.