高效LOS采样GNSS直接位置估计：信息损失CRB分析
Efficient LOS-Sampled GNSS Direct Position Estimation: An Information-Loss CRB Analysis
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中文总结 AI 辅助
本文提出LOS采样GNSS直接位置估计方法，通过残差最小化解决度量不匹配，推导信息损失CRB，在接近全信息性能的同时将相关评估次数从指数降为线性。
中文摘要 AI 辅助
传统全球导航卫星系统（GNSS）直接位置估计（DPE）利用原始中频（IF）数据，并提供全信息克拉美-罗界（CRB）基准，但其累积相关目标函数需要在共同的位置、速度和时间（PVT）搜索空间上进行密集评估。本文提出了一种高效的视线（LOS）采样DPE方法，其中每个卫星通道独立地仅保留与其LOS方向对齐的PVT采样点。构建了一个残差最小化估计器，以解决累积相关度量与每卫星LOS采样之间的不匹配问题。推导了LOS采样DPE的Fisher信息矩阵（FIM）和信息损失CRB，量化了由LOS采样参数决定的信息损失。理论分析、蒙特卡洛仿真和实际实验表明，适当的LOS采样接近全信息CRB和传统DPE的实际性能，同时将相关评估次数从指数增长减少到线性增长。
英文摘要
Conventional Global Navigation Satellite System (GNSS) Direct Position Estimation (DPE) exploits raw intermediate-frequency (IF) data and provides a full-information Cramér-Rao Bound (CRB) benchmark, but its accumulated-correlation objective requires dense evaluations over a common Position, Velocity, and Time (PVT) search space. This paper proposes an efficient Line-of-Sight (LOS)-sampled DPE, where each satellite channel independently retains only PVT sample points aligned with its LOS direction. A residual-minimization estimator is formulated to resolve the mismatch between accumulated-correlation metrics and per-satellite LOS sampling. The Fisher information matrix (FIM) and information-loss CRB of LOS-sampled DPE are derived, quantifying the information loss determined by LOS sampling parameters. Theoretical analysis, Monte Carlo simulations, and real experiments show that proper LOS sampling approaches the full-information CRB and practical performance of conventional DPE, while reducing the number of correlation evaluations from exponential to linear growth.
发表机构
- School of Aeronautics and Astronautics, Shanghai Jiao Tong University（上海交通大学航空宇航学院）
- Ann and H. J. Smead Department of Smead Aerospace Engineering Sciences, University of Colorado Boulder（科罗拉多大学博尔德分校Smead航空航天工程科学系）
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