An Adaptive Unscented Kalman Ilter Integrated Navigation Method Based on the Maximum Versoria Criterion for INS/GNSS...

Area of Science:

• Navigation Systems Engineering
• Control Theory
• Signal Processing

Background:

• Inertial Navigation System/Global Navigation Satellite System (INS/GNSS) integration is crucial for accurate navigation.
• Measurement anomalies and non-Gaussian noise degrade performance in complex environments.
• Conventional filters struggle with dynamic adaptation to these challenges.

Purpose of the Study:

• To develop an adaptive unscented Kalman filter (AUKF) for enhanced INS/GNSS robustness and accuracy.
• To address measurement anomalies and non-Gaussian noise using the maximum versoria criterion (MVC).
• To improve real-time adaptation and navigation parameter tracking.

Main Methods:

• Developed an adaptive unscented Kalman filter (AUKF) with dynamic parameter adjustment.
• Introduced the maximum versoria criterion (MVC) to construct a novel cost function.
• Incorporated high-order moments of estimation errors to suppress non-Gaussian disturbances.

Main Results:

• The proposed AUKF method significantly outperforms traditional filters (EKF, UKF, MCCUKF).
• Reduced root mean square error (RMSE) for velocity and position by over 60%, 50%, and 30%, respectively.
• Demonstrated robust navigation performance, accuracy, and stability in complex environments.

Conclusions:

• The proposed AUKF algorithm effectively handles measurement anomalies and non-Gaussian noise.
• Achieved superior navigation accuracy and stability compared to existing methods.
• Shows significant practical applicability for real-world INS/GNSS navigation systems.