An Adaptive Filtering Method for Cooperative Localization in Leader-Follower AUVs

Area of Science:

• Robotics
• Marine Engineering
• Navigation Systems

Background:

• Autonomous underwater vehicle (AUV) navigation and localization are critical in complex marine environments.
• Conventional Kalman filters struggle with sensor outliers and inaccurate noise covariance, impacting cooperative localization accuracy.
• Leader-follower AUV systems require robust localization for effective coordination.

Purpose of the Study:

• To develop an improved adaptive Kalman filter for robust cooperative localization of multi-AUVs.
• To address the challenges posed by sensor outliers and inaccurate noise covariance in AUV localization.
• To enhance the accuracy and reliability of AUV navigation in dynamic marine settings.

Main Methods:

• Proposed an improved Sage-Husa adaptive extended Kalman filter (improved SHAEKF).
• Implemented a Chi-square test on innovation to detect measurement anomalies.
• Utilized suboptimal maximum a posterior estimation with weighted exponential fading memory for anomaly correction.
• Incorporated online adjustment of the measurement noise covariance matrix.

Main Results:

• The improved SHAEKF effectively identified and mitigated measurement anomalies.
• Online adjustment of the measurement noise covariance matrix improved estimation accuracy.
• Numerical simulations demonstrated significant reductions in average root mean square and standard deviation of localization errors.
• The proposed method proved effective for leader-follower multi-AUV cooperative localization.

Conclusions:

• The improved SHAEKF offers enhanced robustness and accuracy for AUV cooperative localization.
• The algorithm effectively handles sensor outliers and adapts to changing environmental conditions.
• This advancement is crucial for reliable navigation and coordination of multi-AUV systems in marine applications.