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

Area of Science:

• Navigation Systems Engineering
• Inertial Navigation
• Signal Processing

Background:

• Strapdown inertial navigation systems (SINSs) require rapid initial attitude determination.
• Coarse alignment methods are crucial for enhancing SINS initialization speed without external aids.
• Inertial sensor errors and external disturbances limit the accuracy of traditional coarse alignment observation vector models.

Purpose of the Study:

• To develop an improved coarse alignment algorithm for SINSs.
• To address the limitations of observation vector model inaccuracy in existing methods.
• To enhance the speed and accuracy of initial attitude determination under swaying-base conditions.

Main Methods:

• Established an apparent velocity vector observation model.
• Designed a sliding-window vector integration algorithm to mitigate cumulative observation vector errors.
• Implemented a vector reconstruction model utilizing the Sage-Husa adaptive Kalman filter (AKF) for self-alignment.

Main Results:

• Simulations and turntable experiments validated the proposed algorithm.
• The vector reconstruction approach significantly improved alignment accuracy.
• The method demonstrated superior performance compared to existing coarse alignment techniques.

Conclusions:

• The proposed Sage-Husa AKF-based vector reconstruction coarse alignment algorithm effectively enhances SINS initial attitude determination.
• The method overcomes limitations associated with observation vector inaccuracy and external disturbances.
• This approach offers a robust solution for rapid and accurate SINS alignment in dynamic environments.