Optimized Fractional-Order Extended Kalman Filtering for IMU-Based Attitude Estimation Using the Hippopotamus...

Area of Science:

• Control Systems Engineering
• Optimization Algorithms
• Robotics and Autonomous Systems

Background:

• Fractional-order Extended Kalman Filter (FEKF) performance is limited by manual tuning of its fractional-order parameter.
• Accurate attitude estimation is crucial for sensor fusion in various applications.

Purpose of the Study:

• To develop an automated framework (HO-FEKF) for optimizing the fractional-order parameter of FEKF.
• To enhance the FEKF model for improved handling of nonlinear system dynamics and sensor fusion.

Main Methods:

• Integration of the Hippopotamus Optimization (HO) algorithm for automated parameter tuning.
• Implementation of a hierarchical optimization strategy to minimize attitude estimation error.
• Enhancements to the FEKF model, including improved Jacobian calculations, handling of cross-factor interactions, and a sliding residual window.

Main Results:

• The enhanced FEKF model demonstrated superior performance compared to the traditional FEKF.
• The complete HO-FEKF framework significantly outperformed FEKF combined with other optimization algorithms (GA, GWO, HHO, HiPPO-LegS).
• Validation on both public benchmark and custom datasets confirmed the effectiveness of the proposed method.

Conclusions:

• HO-FEKF offers an effective solution for adaptive, high-accuracy attitude estimation.
• The automated tuning approach overcomes limitations of manual parameter adjustment.
• The framework shows significant practical potential for real-world sensor fusion applications.