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GNSS/SINS/DVL integrated navigation algorithm based on adaptive differential Kalman filtering
Zhao Zhan1, Changjian Liu1, Kaidi Jin2
1Information Engineering University, Zhengzhou, China.
Plos One
|February 5, 2026
Summary
This study introduces an adaptive differential Kalman filtering (ADKF) method to improve navigation accuracy for unmanned underwater vehicles (UUVs). The ADKF method enhances parameter estimation stability and accuracy in complex marine environments compared to standard Kalman filters.
Area of Science:
- Marine Navigation Technology
- Robotics and Autonomous Systems
- Signal Processing and Control Theory
Background:
- Integrated navigation systems, combining Global Navigation Satellite System/Strapdown Inertial Navigation System/Doppler Velocity Logger (GNSS/SINS/DVL), are crucial for marine carriers.
- Unmanned Underwater Vehicles (UUVs) encounter significant challenges in dynamic maritime navigation due to observation anomalies and inaccurate dynamic models.
- Standard Kalman filters (KF) struggle to maintain accuracy in complex ocean environments, impacting UUV navigation parameter estimation.
Purpose of the Study:
- To develop an improved filtering method for GNSS/SINS/DVL integrated navigation data specifically for UUVs.
- To address the limitations of standard Kalman filters in handling dynamic maritime navigation complexities.
- To enhance the accuracy and stability of navigation parameter estimation for UUVs in challenging marine settings.
Main Methods:
- Implementation of an Adaptive Differential Kalman Filtering (ADKF) algorithm.
- Processing of integrated navigation data from GNSS/SINS/DVL subsystems.
- Comparative analysis of ADKF against the standard Kalman filter (KF) using experimental data.
Main Results:
- The proposed ADKF method demonstrated significantly enhanced accuracy in navigation parameter estimation.
- The ADKF algorithm showed improved stability in parameter estimation compared to the standard KF.
- Experimental results confirmed the effectiveness of ADKF in complex marine environments.
Conclusions:
- The adaptive differential Kalman filtering (ADKF) method is a robust solution for post-processing integrated navigation data for UUVs.
- ADKF offers superior performance over standard KF in terms of accuracy and stability for UUV navigation.
- The developed method is well-suited for enhancing the reliability of UUV navigation in challenging ocean conditions.
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