Variational Bayesian Innovation Saturation Kalman Filter for Micro-Electro-Mechanical System-Inertial Navigation...

Area of Science:

• Navigation Systems Engineering
• Signal Processing
• Robust Control Theory

Background:

• Micro-electro-mechanical system-inertial navigation systems (MEMS-INS) integrated with polarization compass (PC) face challenges from time-varying measurement noise, heavy-tailed distributions, and outliers, especially under disturbances.
• These noise characteristics degrade the performance and accuracy of traditional Kalman filtering approaches in integrated navigation systems.

Purpose of the Study:

• To propose an improved Variational Bayesian Innovation Saturation Robust Adaptive Kalman filter (VISKF) algorithm to address the limitations of existing methods in handling noisy and outlier-corrupted measurements from PC sensors.
• To enhance the robustness and accuracy of MEMS-INS/PC integrated navigation systems operating in complex and disturbed environments.

Main Methods:

• Utilized the variational Bayesian (VB) method with Student's t-distribution (STD) to accurately estimate statistical properties of time-varying PC measurement noise.
• Introduced an innovation saturation function with an adaptive update strategy for the saturation boundary, employing a two-layer structure to manage innovation value divergence caused by outliers.
• Conducted static and dynamic experiments on an unmanned vehicle to validate the proposed VISKF algorithm's performance.

Main Results:

• The VISKF algorithm demonstrated significant improvements in dynamic orientation accuracy compared to Adaptive Kalman Filter (AKF), Variational Bayesian Robust Adaptive Kalman Filter (VBRAKF), and Innovation Saturate Robust Adaptive Kalman Filter (ISRAKF), achieving improvements of 76.89%, 67.23%, and 84.45%, respectively.
• The proposed algorithm exhibited superior performance and robustness over other comparable methods in complex environments.
• Accurate estimation of measurement noise statistical parameters was achieved through the VB-STD approach.

Conclusions:

• The developed VISKF algorithm effectively mitigates the impact of heavy-tailed noise and outliers in MEMS-INS/PC integrated navigation systems.
• The adaptive innovation saturation mechanism significantly enhances the system's robustness and accuracy.
• This method offers a substantial improvement in navigation accuracy and reliability for INS/PC integrated systems operating under challenging conditions.