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Multi-Sensor Fusion for Underwater Vehicle Localization by Augmentation of RBF Neural Network and Error-State Kalman

Nabil Shaukat¹, Ahmed Ali¹, Muhammad Javed Iqbal¹

¹Oceanic Engineering Research Institute, University of Malaga, 29010 Malaga, Spain.

Sensors (Basel, Switzerland)

|February 10, 2021

Summary

This study introduces a novel radial basis function (RBF) neural network augmented error-state Kalman filter (ESKF) for improved underwater vehicle localization. The new method enhances navigation accuracy in nonlinear conditions.

Keywords:

RBF localization multi-sensor fusion navigation underwater robotics underwater vehicle

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Area of Science:

Robotics
Navigation Systems
Artificial Intelligence

Background:

Extended Kalman Filter (EKF) and Error-State Kalman Filter (ESKF) are standard for underwater multi-sensor fusion.
First-order Taylor series approximation in EKF/ESKF limits accuracy in highly nonlinear environments.

Purpose of the Study:

To develop a novel multi-sensor fusion algorithm for enhanced underwater vehicle localization.
To improve state estimation accuracy by augmenting the ESKF with a radial basis function (RBF) neural network.

Main Methods:

Augmented the Error-State Kalman Filter (ESKF) with a Radial Basis Function (RBF) neural network.
Optimized RBF network weights and centers using steepest descent to minimize mean square error (MSE).
Validated the RBF-augmented ESKF using Monte Carlo simulations in realistic underwater scenarios.

Main Results:

The proposed RBF-augmented ESKF demonstrated superior localization and navigation performance compared to conventional ESKF.
The algorithm effectively mitigated accuracy degradation caused by high nonlinearity and modeling uncertainty.
The RBF network improved the innovation error term, compensating for ESKF limitations.

Conclusions:

The RBF-augmented ESKF offers a significant advancement for underwater vehicle navigation and localization.
This approach provides robust performance even amidst complex environmental factors and system uncertainties.
The integration of neural networks with Kalman filtering presents a promising direction for sensor fusion applications.