用GNSS历史数据推算加速度，提升导航精度
Enhanced INS/GNSS State Estimation using GNSS-Based Acceleration Measurements
- 用历史GNSS数据与运动模型估算车辆加速度
- 两个实测数据集定位均方根误差分别降低11.4%和20.7%
- 特别适合低动态场景下惯性导航的精度提升
精准可靠的导航对自主地面车辆至关重要。传统惯性/全球导航卫星系统（INS/GNSS）融合依赖GNSS位置更新，但在低动态运动时对姿态和惯性传感器误差状态的可观测性有限。本文提出利用历史GNSS测量值结合运动模型，提取有意义的车辆加速度信息，并将其融入INS/GNSS滤波器中，以增强其鲁棒性和精度。该方法在两个来自不同移动平台和惯性传感器等级的真实无人地面车辆数据集上进行了评估。结果表明，相较于标准位置辅助滤波器，定位精度持续提升，两个数据集的平均位置均方根误差分别降低了11.40%和20.74%。
原文摘要 · Abstract (English)
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.
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