Equivariant Filter Design for Acoustic and Depth Aided Inertial Navigation Systems

2026-09-17

AI Summary

该研究通过设计等变滤波器解决AUV无GPS导航精度问题，利用Tangent-Group对称性改进了状态估计和协方差计算。

Abstract

Autonomous Underwater Vehicles (AUVs) navigating without GPS typically fuse inertial measurements with acoustic Doppler Velocity Log (DVL) velocities and pressure-derived depth. Posing the navigation state on a Lie group improves accuracy and consistency. However, state-of-the-art filters based on the Invariant Extended Kalman Filter (IEKF) append the Inertial Measurement Unit (IMU) biases as a Euclidean extension, which breaks the group-affine structure required for exact log-linear error dynamics, causing the reported covariance to degrade alongside the estimate. We apply the Tangent-Group (TG) symmetry, which carries the biases within the geometry of the state space, to derive an Equivariant Filter (EqF) for this system, leaving zero linearization error in the navigation states and second-order error only in the biases. We develop an equivariant output model for the DVL, whose update incurs only third-order linearization error, together with a direct pressure output. Monte Carlo simulations benchmark the TG-EqF against a Two-Frame-Group IEKF and a Multiplicative EKF. The TG-EqF reduces error by 18--25\% against both alternatives in each of attitude, velocity, and position. The main benefit is in the covariance it estimates: its Average Normalized Estimation Error Squared (ANEES) stays closer to its nominal value of one than that of the others. Offline analysis on AUV field data corroborates the findings of the simulations, demonstrating reduced position drift.

Problem

• Equivariant Filter
• Inertial Navigation Systems
• Lie Group
• Doppler Velocity Log
• Covariance

Innovation

• Equivariant Filter
• Tangent-Group Symmetry
• Inertial Navigation
• Acoustic Doppler Velocity Log
• Lie Group