In-Motion Forward-Forward Backtracking Fine Alignment Based on Displacement Observation for SINS/GNSS.

Area of Science:

• Navigation Systems Engineering
• Inertial Navigation
• Signal Processing

Background:

• Traditional fine alignment algorithms for Strapdown Inertial Navigation Systems (SINS) using linear Kalman filtering suffer from slow convergence.
• This limitation hinders the efficiency and accuracy of initial alignment processes in navigation systems.
• Re-analysis of the fine alignment model is necessary to address these convergence issues.

Purpose of the Study:

• To propose a novel forward-forward backtracking fine alignment algorithm for SINS.
• To enhance the convergence speed and accuracy of SINS initial alignment.
• To validate the effectiveness and feasibility of the proposed algorithm through simulations and vehicle tests.

Main Methods:

• Derived a forward-forward backtracking fine alignment model in the initial navigation frame.
• Utilized the carrier's displacement vector, obtained from Global Navigation Satellite System (GNSS) positioning, as the observation for the fine alignment model.
• Improved initial alignment convergence by backtracking and reusing a subset of navigation data.

Main Results:

• The proposed algorithm significantly improved the convergence speed compared to traditional methods.
• Each backtracking alignment step enhanced the fine alignment accuracy to meet initial alignment performance requirements.
• Simulation and vehicle test results demonstrated the effectiveness and feasibility of the backtracking fine alignment algorithm.

Conclusions:

• The forward-forward backtracking fine alignment algorithm offers a viable solution to the slow convergence problem in SINS initial alignment.
• The method's ability to reuse navigation data effectively boosts both speed and accuracy.
• This approach proves practical for real-world navigation system applications.