Analytical Covariance Propagation for DVL-Aided Loosely Coupled SINS Under Attitude Uncertainty

Published August 26, 2026 · Category: Robotics

Overview

arXiv:2601.19509v2 Announce Type: replace Abstract: In loosely coupled strapdown inertial navigation system/Doppler velocity log (SINS/DVL) integration, the body-frame DVL velocity is projected into the navigation frame using the SINS-computed attitude. Attitude uncertainty consequently affects both the projected velocity observation and its associated measurement covariance. This paper develops an analytical covariance propagation (ACP) method that treats these two effects consistently. The attitude-error-aware observation model represents perturbations in the projected DVL velocity, whereas the covariance construction propagates the body-frame DVL covariance at the matrix level and adds a closed-form term derived from the predicted attitude-error covariance, rather than directly rotating component-wise standard deviations. Simulation and a controlled surface-water field experiment compare ACP with conventional SINS/DVL and adaptive-covariance benchmarks. For the evaluated data sets and parameterizations, ACP yields the lowest position-error metrics among the compared methods. Relative to the variational Bayesian adaptive Kalman filter (VBAKF) in the field experiment, ACP reduces the northward, eastward, and downward position root-mean-square errors (RMSEs) by 48.5%, 55.0%, and 74.8%, respectively. Beyond DVL aiding, ACP provides a transferable covariance-construction principle for attitude-dependent vector-measurement fusion and a physically interpretable alternative to heuristic frame-dependent covariance assignment.

Source

Originally published at arxiv.org.

Related Articles

• Vision-Language-Action Safety: Threats, Challenges, Evaluations, and Mechanisms
• A Durable Vision-Based Tactile Fingertip for Robotic Manipulation
• CARO: Contact-Agnostic Residual Observation for Zero-Shot Robust Quadruped Locomotion