A New Variational Bayesian Adaptive Extended Kalman Filter for Cooperative Navigation

Area of Science:

• Robotics
• Navigation Systems
• Signal Processing

Background:

• Underwater cooperative navigation faces challenges from unknown state and measurement noises.
• Master-slave Autonomous Underwater Vehicle (AUV) systems require robust navigation solutions.
• Traditional Kalman filters struggle with uncertain noise parameters.

Purpose of the Study:

• To develop an adaptive filter for AUVs that addresses unknown state and measurement noises.
• To enhance the accuracy and reliability of underwater cooperative navigation.
• To introduce a novel Variational Bayesian approach for adaptive filtering.

Main Methods:

• Proposed a Variational Bayesian (VB)-based Adaptive Extended Kalman Filter (VBAEKF).
• Utilized Inverse Wishart (IW) distribution to model predicted error covariance and measurement noise covariance.
• Employed VB approximation for adaptive estimation of state, predicted error covariance, and measurement noise covariance.

Main Results:

• The VBAEKF adaptively estimates system states and noise covariances.
• Demonstrated improved performance in master-slave AUV navigation compared to standard methods.
• Successful validation through a practical lake trial.

Conclusions:

• The VBAEKF effectively handles unknown noises in underwater cooperative navigation.
• The proposed method offers a significant advantage for AUV navigation systems.
• VB approximation provides a robust framework for adaptive state and noise estimation.