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A Novel Factor Graph and Cubature Kalman Filter Integrated Algorithm for Single-Transponder-Aided Cooperative
Wanlong Zhao1, Huifeng Zhao2, Deyue Zou3
1School of Information Science and Engineering, Harbin Institute of Technology, Weihai 264209, China.
Entropy (Basel, Switzerland)
|October 23, 2021
Summary
This study introduces a new Factor Graph and Cubature Kalman Filter (FGCKF) algorithm to enhance cooperative localization (CL) for multiple underwater autonomous underwater vehicles (AUVs). The FGCKF algorithm improves positioning accuracy and robustness in challenging underwater environments.
Area of Science:
- Robotics
- Marine Engineering
- Signal Processing
Background:
- Cooperative localization (CL) is essential for multi-AUV operations.
- Single-transponder-aided cooperative localization (STCL) offers a promising approach.
- Improving STCL accuracy and robustness is critical for reliable underwater navigation.
Purpose of the Study:
- To propose a novel Factor Graph and Cubature Kalman Filter (FGCKF)-integrated algorithm.
- To enhance the positioning accuracy and robustness of STCL for multi-AUVs.
- To address challenges posed by uncertain observation environments and data outliers.
Main Methods:
- Integration of Factor Graph and Cubature Kalman Filter (FGCKF).
- Utilizing historical information for efficient measurement updating.
- Employing Adaptive CKF, sum product, and Maximum Correntropy Criterion (MCC) for outlier rejection.
Main Results:
- The FGCKF algorithm demonstrated improved positioning accuracy compared to traditional methods.
- Enhanced robustness was observed in simulations and experimental validation.
- Effective handling of outliers in acoustic transmission delay, sound velocity, and motion velocity.
Conclusions:
- The proposed FGCKF algorithm significantly improves multi-AUV cooperative localization.
- The method offers superior accuracy and robustness for underwater navigation.
- FGCKF is a viable solution for reliable autonomous underwater vehicle operations.
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