发表机构
Space & Terrestrial Autonomous Robotic Systems (STARS) Laboratory at the University of Toronto Institute for Aerospace Studies (UTIAS); Autonomous Robotics & Convex Optimization (ARCO) Laboratory in the Department of Computing and Software, McMaster University（多伦多大学航天研究所空间与地面自主机器人系统（STARS）实验室; 麦克马斯特大学计算与软件系自主机器人与凸优化（ARCO）实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究辅助导航中单个辅助传感器测量相对于惯性测量流有未知但恒定延迟时系统的可识别性，利用特殊伽利略群刻画无信息轨迹并与延迟测量模型连续对称性相关，揭示可识别性失败轨迹类别及与线性化分析联系。
AI 中文摘要
在许多多传感器系统中，不同传感器测量存在未知相对时间延迟。准确状态估计需考虑并校准延迟。本文考虑辅助导航情况，研究单个辅助传感器测量相对于惯性测量流有未知但恒定延迟时系统的可识别性。可识别性不仅取决于测量时间结构，还取决于车辆轨迹形状。利用特殊伽利略群刻画无信息轨迹并与延迟测量模型连续对称性相关，揭示可识别性失败轨迹类别比之前报道的更大，并将此刻画与基于雅可比矩阵的线性化分析联系起来。尽管本文研究受辅助导航启发，但基本思想更广泛适用于具有延迟测量的李群上的估计问题。
英文摘要
In many multisensor systems, measurements from different sensors are subject to unknown relative time delays. Accurate state estimation requires that delays be accounted for and, when possible, calibrated online. We consider the case of aided inertial navigation, where measurements from a single aiding sensor are subject to an unknown but constant delay relative to the inertial measurement stream, and study the identifiability of the resulting system. Critically, identifiability depends not only on the temporal structure of the measurements, but also on the shape of the vehicle trajectory: some trajectories are sufficiently informative to support unique recovery of the delay and the navigation state, while others are not. Using the special Galilean Lie group, we characterize a broad family of uninformative trajectories, each generated by a constant element of the Galilean Lie algebra. We show that, along any such trajectory, the delayed measurement model admits a continuous symmetry that prevents unique recovery of the delay and the navigation state. We connect this symmetry-based characterization to the familiar linearized, Jacobian-based analysis. Although our development is motivated by aided navigation, the underlying ideas apply more generally to estimation problems on Lie groups with delayed measurements.
CommentsAccepted to the IEEE International Conference on Multisensor Fusion and Integration (MFI), Pilsen, Czechia, Sep 2-4, 2026
