Fuzzy Logic-Based Adaptive Filtering for Transfer Alignment

Area of Science:

• Navigation Systems
• Control Theory
• Signal Processing

Background:

• Strapdown inertial navigation systems (SINS) require accurate transfer alignment for airborne tactical vehicles.
• System model errors in Kalman filters degrade SINS state estimation accuracy.

Purpose of the Study:

• To develop a fuzzy logic-based adaptive filtering method for SINS transfer alignment.
• To mitigate the impact of system model errors on SINS state estimation.

Main Methods:

• Designed a fuzzy logic-based adaptive filtering approach for SINS transfer alignment.
• Embedded state and measurement error models with residuals into the Kalman filter framework.
• Utilized fuzzy rules to estimate system measurement and predicted state covariances by minimizing residuals.

Main Results:

• The proposed method effectively handles system model errors in SINS transfer alignment.
• Achieved at least 18.83% higher accuracy compared to benchmark methods.
• Simulations and experiments validated the method's performance.

Conclusions:

• The fuzzy logic adaptive filter significantly improves SINS transfer alignment accuracy.
• This approach offers a robust solution for airborne tactical vehicle navigation.
• The method demonstrates superior performance in managing model uncertainties.