Generalized Maximum Correntropy Cubature Kalman Filter with Variational Bayesian for SINS/GPS Integrated Navigation...

Area of Science:

• Navigation Systems Engineering
• Signal Processing
• Control Theory

Background:

• Strapdown Inertial Navigation Systems (SINS) and Global Positioning Systems (GPS) integrated navigation systems suffer from degraded accuracy and robustness under time-varying non-Gaussian measurement noises.
• Existing filtering methods struggle to adapt to the dynamic statistical characteristics of these complex noise environments.

Purpose of the Study:

• To propose a novel filtering method that enhances the accuracy and robustness of SINS/GPS integrated navigation systems.
• To effectively address time-varying non-Gaussian measurement noises in integrated navigation systems.

Main Methods:

• The study proposes a variational Bayesian generalized maximum correntropy cubature Kalman filter (VBGMCCKF).
• This method integrates variational Bayesian adaptive techniques with the generalized maximum correntropy criterion.
• The filter is designed to handle noises with time-varying statistical characteristics and various non-Gaussian noise types.

Main Results:

• VBGMCCKF demonstrates superior estimation accuracy compared to existing methods in SINS/GPS integrated navigation.
• The proposed filter exhibits enhanced robustness when subjected to different simulated scenarios with complex noise.
• The method effectively improves applicability to diverse non-Gaussian noise conditions.

Conclusions:

• The VBGMCCKF is an effective solution for integrated navigation systems operating under complex and time-varying non-Gaussian noise environments.
• The proposed method significantly improves the performance of SINS/GPS integrated navigation systems, offering greater reliability and accuracy.