An Online Detection and Rejection Method for Consecutive Outliers in Underwater Long-Baseline Positioning Based on...

Area of Science:

• Marine robotics
• Underwater navigation
• Signal processing

Background:

• Long-baseline (LBL) positioning systems are crucial for underwater navigation but are susceptible to high-magnitude outlier interference in complex marine environments.
• Existing methods struggle with persistent and consecutive outlier data, compromising positioning accuracy.

Purpose of the Study:

• To develop a robust algorithm for mitigating outlier interference in LBL positioning systems.
• To enhance the positioning accuracy of Autonomous Underwater Vehicles (AUVs) in challenging underwater scenarios.

Main Methods:

• A kinematic constraint-based Robust Interacting Multiple Model Kalman Filter algorithm is proposed.
• The algorithm integrates anchor point initialization, multi-step historical observations, a spatial Euclidean distance discriminant, and AUV maximum velocity constraints.
• A measurement mask matrix is introduced within the Kalman Filter recursion to isolate and exclude outliers from state updates.

Main Results:

• The proposed algorithm significantly improves outlier identification and suppression compared to standard LBL positioning, single outlier detection, and the conventional Maximum Correntropy Criterion-based Kalman Filter (MCC-KF).
• Enhanced performance is particularly evident under consecutive anomaly conditions.
• Positioning accuracy for maneuvering targets in complex underwater environments is demonstrably improved.

Conclusions:

• The developed kinematic constraint-based robust Kalman filter effectively addresses persistent outlier interference in LBL positioning systems.
• The algorithm offers a superior solution for accurate underwater navigation of AUVs in complex environments.
• This approach provides a reliable method for real-time outlier rejection, enhancing the robustness of underwater positioning.