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Sensor Fusion for Underwater Vehicle Navigation Compensating Misalignment Using Lie Theory.

Da Bin Jeong¹, Nak Yong Ko¹

¹Department of Electronic Engineering, Interdisciplinary Program in IT-Bio Convergence Systems, Chosun University, Gwangju 61452, Republic of Korea.

Sensors (Basel, Switzerland)

|March 13, 2024

Summary

This study introduces a novel sensor fusion method for unmanned underwater vehicle navigation. By integrating Lie theory with Kalman filtering, it significantly enhances navigation accuracy and system stability.

Keywords:

Kalman filter Lie theory attitude misalignment navigation underwater vehicle

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Area of Science:

Robotics
Navigation Systems
Control Theory

Background:

Unmanned underwater vehicles (UUVs) require accurate navigation for mission success.
Sensor fusion, combining data from Inertial Navigation Systems (INS) and Doppler Velocity Logs (DVL), is crucial for UUV navigation.
Existing Extended Kalman Filter (EKF) methods struggle with sensor misalignment due to approximations in non-differentiable spaces.

Purpose of the Study:

To develop an advanced sensor fusion method for UUV navigation.
To accurately estimate and compensate for sensor misalignment between INS and DVL.
To improve the overall accuracy and stability of UUV navigation systems.

Main Methods:

A novel sensor fusion approach integrating Lie theory into the Kalman filter framework.
Utilizing the 3-dimensional Euclidean group (SE(3)) for pose representation and the 3-sphere space (S3) for misalignment, expressed using unit quaternions.
Employing Lie algebra for exact differentiation in pose and misalignment increments, enabling an enhanced EKF (EKF).

Main Results:

The proposed Lie theory-based EKF method achieves enhanced navigation accuracy compared to traditional approaches.
Exact differentiation in differentiable spaces, enabled by Lie algebra, overcomes limitations of previous EKF approximations.
Improved convergence and stability of internal Kalman filter parameters, including Kalman gain and measurement innovation.

Conclusions:

The integration of Lie theory provides a mathematically rigorous framework for UUV sensor fusion.
The developed method significantly improves the accuracy and reliability of UUV navigation systems.
This approach offers a robust solution for estimating and compensating sensor misalignment in underwater vehicles.