A Polar Robust Kalman Filter Algorithm for DVL-Aided SINSs Based on the Ellipsoidal Earth Model

Area of Science:

• Oceanography
• Robotics
• Navigation Systems

Background:

• Autonomous underwater vehicles (AUVs) are crucial for polar ocean exploration.
• Traditional inertial navigation systems fail in polar regions due to meridian convergence.
• Doppler velocity log (DVL) data is susceptible to outlier noise in harsh polar environments.

Purpose of the Study:

• To develop a robust navigation mechanism for AUVs operating in polar regions.
• To improve the accuracy and reliability of AUV positioning despite DVL outliers.
• To address the limitations of standard Kalman filters in polar navigation.

Main Methods:

• Designed a transverse inertial navigation mechanism utilizing an earth ellipsoidal model.
• Implemented a robust Kalman filter algorithm incorporating Mahalanobis distance.
• Adaptively estimated measurement noise covariance to modify Kalman filter gain.

Main Results:

• The proposed algorithm effectively resists the influence of DVL outliers.
• Positioning accuracy of AUVs in polar regions was significantly improved.
• Validated through trial ship and semi-physical simulation experiments.

Conclusions:

• The developed transverse inertial navigation mechanism enhances AUV performance in polar exploration.
• The robust Kalman filter approach successfully mitigates DVL outlier noise.
• This method offers a reliable solution for accurate AUV navigation in challenging polar conditions.