A Multi-Mode Switching Variational Bayesian Adaptive Kalman Filter Algorithm for the SINS/PNS/GMNS Navigation System
Jie Zhang1, Shanpeng Wang2, Wenshuo Li3
1School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China.
Sensors (Basel, Switzerland)
|May 20, 2022
Summary
A new multi-mode switching variational Bayesian adaptive Kalman filter (MMS-VBAKF) algorithm enhances ship navigation in uncertain ocean environments. This adaptive filter improves the reliability and accuracy of integrated navigation systems.
Area of Science:
- Marine engineering
- Navigation systems
- Signal processing
Background:
- Ocean environments present complex challenges for ship navigation.
- Integrated navigation systems like SINS/PNS/GMNS face interference.
- Existing methods struggle with environmental uncertainty.
Purpose of the Study:
- To propose a novel algorithm for robust integrated navigation.
- To address complex interference in pelagic ocean environments.
- To enhance the accuracy and reliability of ship navigation systems.
Main Methods:
- Developed a multi-mode switching variational Bayesian adaptive Kalman filter (MMS-VBAKF).
- Designed an interference evaluation and multi-mode switching mechanism using polarization and geomagnetic data.
- Implemented adaptive estimation of measurement noise statistics and system states.
Main Results:
- The MMS-VBAKF algorithm effectively handles slight, tolerable, and excessive interference.
- Adaptive estimation of noise statistics and system states was achieved in real-time.
- Simulation experiments validated the algorithm's effectiveness.
Conclusions:
- The proposed MMS-VBAKF algorithm significantly improves navigation reliability, robustness, and accuracy.
- The interference evaluation and switching mechanism is crucial for adaptive filtering.
- This approach offers a promising solution for challenging marine navigation scenarios.
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