Enhanced INS/GNSS State Estimation using GNSS-Based Acceleration Measurements

2026-05-23

AI Summary

This study addresses the limited observability of attitude and inertial sensor errors in traditional INS/GNSS integration under low-dynamic conditions. To overcome this challenge, the authors propose a novel approach that, for the first time, incorporates vehicle acceleration—derived from historical GNSS measurements and a kinematic model—as an additional observation within an extended Kalman filter framework. This augmentation significantly enhances system observability, thereby improving positioning accuracy and robustness in low-dynamic scenarios. Experimental validation on two real-world unmanned ground vehicle datasets demonstrates consistent performance gains, achieving average reductions of 11.40% and 20.74% in position root-mean-square error, respectively.

Abstract

Accurate and reliable navigation is essential for autonomous ground vehicle operations. Standard INS/GNSS fusion relies on GNSS position updates, which provide limited observability of orientation and inertial sensor error states, particularly during low-dynamic motion. In this work, we propose utilizing past GNSS measurements alongside a motion model to extract meaningful vehicle acceleration information. This acceleration measurement is then integrated into the INS/GNSS filter to improve its robustness and accuracy. The proposed approach is evaluated on two real-world unmanned ground vehicle datasets collected from different mobile platforms and inertial sensor grades. Results demonstrate consistent positioning accuracy improvements relative to the standard position-aided filter, with mean position root mean square error improvements of 11.40 % and 20.74 % on the two datasets, respectively.

Problem

• INS/GNSS fusion
• low-dynamic motion
• observability
• position accuracy
• inertial sensor errors

Innovation

• GNSS-based acceleration
• INS/GNSS fusion
• state estimation
• low-dynamic motion
• motion model