An Alignment Method Based on KF-ASMUKF Hybrid Filtering for Ship's SINS under Mooring Conditions.

Area of Science:

• Navigation Systems Engineering
• Control Theory
• Signal Processing

Background:

• Strapdown inertial navigation systems (SINS) face alignment accuracy degradation with increased nonlinear filtering state dimensions under mooring conditions.
• Traditional filtering methods struggle with the computational complexity and accuracy demands of modern marine navigation.

Purpose of the Study:

• To develop an improved filtering algorithm for ship SINS alignment under mooring conditions.
• To enhance alignment accuracy and reduce computational load compared to existing methods.

Main Methods:

• A hybrid filtering model, KF-ASMUKF, combining Kalman Filter (KF) and Adaptive scale mini-skewness Unscented Kalman Filter (ASMUKF).
• Implementation of a novel sampling strategy using scale mini-skewness single line sampling with an adaptive scale factor within the UKF.
• Division of the filtering model into linear (KF) and nonlinear (ASMUKF) parts for optimized processing.

Main Results:

• The KF-ASMUKF hybrid filtering algorithm demonstrates superior performance over traditional algorithms.
• Significant reduction in the computational burden for ship SINS.
• Marked improvement in SINS alignment accuracy under mooring conditions.

Conclusions:

• The proposed KF-ASMUKF hybrid filtering algorithm effectively addresses the limitations of traditional methods for ship SINS alignment.
• This approach offers a viable solution for enhancing navigation precision and efficiency in marine applications.
• The adaptive scale factor and novel sampling strategy are key to the algorithm's improved performance.