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Updated: Sep 21, 2025

Dynamic Navigation for Dental Implant Placement

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A New Robust Adaptive Filter Aided by Machine Learning Method for SINS/DVL Integrated Navigation System.

Jiupeng Zhu¹, An Li¹, Fangjun Qin¹

¹Department of Navigation Engineering, Naval University of Engineering, Wuhan 430033, China.

Sensors (Basel, Switzerland)

|May 28, 2022

Summary

This summary is machine-generated.

This study introduces an adaptive filter for Strapdown Inertial Navigation Systems/Doppler Velocity Loggers (SINS/DVL) to improve underwater navigation accuracy. The new method enhances robustness and adaptiveness in complex environments.

Keywords:

SINS/DVL integrated system Variational Bayesian adaptive Kalman filter robust and adaptive support vector regression

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Area of Science:

Marine technology
Navigation systems
Signal processing

Background:

Accurate underwater navigation and positioning are crucial for efficient underwater operations.
The performance of integrated Strapdown Inertial Navigation Systems/Doppler Velocity Loggers (SINS/DVL) is significantly impacted by environmental complexity and changes.
Existing systems require enhanced robustness and adaptability to overcome these challenges.

Purpose of the Study:

To propose a novel adaptive filter for SINS/DVL integrated navigation systems.
To improve the robustness and adaptiveness of underwater navigation systems.
To enhance overall navigation accuracy in challenging underwater environments.

Main Methods:

Development of a new adaptive filter utilizing support vector regression.
Implementation of an external sensor-based outlier elimination strategy for Doppler Velocity Logger (DVL) measurements.
Adoption of a Variational Bayesian (VB) strategy to mitigate the impact of process and measurement noise.

Main Results:

The proposed method effectively improves the robustness of the SINS/DVL integrated navigation system by addressing outliers externally.
The Variational Bayesian strategy enhances the filter's adaptiveness by reducing noise influence.
Simulation and ship-borne tests demonstrated superior navigation accuracy compared to existing methods.

Conclusions:

The novel adaptive filter significantly enhances the stability and accuracy of SINS/DVL integrated navigation.
External outlier elimination and Variational Bayesian inference are key to improving system performance.
The developed method offers a more reliable solution for underwater navigation and positioning tasks.