空间GNSS信号的快照跟踪:以月球距离为例研究

Andrea Nardin, Alex Minetto, Salvatore Guzzi, Fabio Dovis, Lauren Konitzer, Joel J. K. Parker
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引用次数: 0

摘要

最新的太空任务揭示了GNSS在距离地球表面超过18.7万公里的距离上的可用性。超过这一高度的GNSS信号的实际可用性和可用性仍然值得怀疑,并且仍然缺乏实验证据。月球GNSS接收器实验(LuGRE)是美国宇航局和意大利航天局(ASI)的联合有效载荷,旨在通过其向月球的轨迹演示基于GNSS的定位、导航和定时。2024年发射后,有效载荷将在不同的任务阶段接收多频全球定位系统(GPS)和伽利略信号,并将进行机载和地面科学实验。除了定位和原始GNSS观测外,LuGRE有效载荷还将提供GNSS数字信号样本的快照。这种快照将是一系列科学调查的核心,并且需要在整个任务期间在LuGRE地面段内运行的后处理单元的开发。在本文中,我们提出了一种分析方法,旨在确定适合于沿月球转移轨道和月球表面记录信号的成功后处理跟踪的最小快照持续时间。在LuGRE框架中,对与实际任务相关的信号进行了处理,以调整接收机架构并研究跟踪性能。随后,利用蒙特卡罗方法对跟踪锁定条件进行了统计分析,以表征接收器前端不同设置的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Snapshot Tracking of GNSS Signals in Space: A Case Study at Lunar Distances
The latest space missions have unveiled GNSS usability for distances greater than 187 000 km from the Earth’s surface. The actual availability and usability of GNSS signals beyond such an altitude are still questionable, and experimental evidence still lacks. The Lunar GNSS Receiver Experiment (LuGRE) is a joint NASA-Italian Space Agency (ASI) payload aiming at demonstrating GNSS-based positioning, navigation, and timing through its trajectory towards the Moon. After the launch in 2024, the payload will receive multi-frequency Global Positioning System (GPS) and Galileo signals across the different mission phases, and will conduct onboard and ground-based scientific experiments. Besides positioning and raw GNSS observables, the LuGRE payload will deliver snapshots of GNSS digital signal samples. Such snapshots will be at the core of a set of science investigations, and require the development of a post-processing unit being operated within the LuGRE ground segment throughout the mission. In this paper, we present an analysis that aims at identifying a minimum snapshot duration suitable for a successful, post-processing tracking of the recorded signal along the Moon transfer orbit and on the Moon surface. The processing of realistic mission-related signals has been performed to tune the receiver architecture and investigate the tracking performance in the LuGRE framework. Subsequently, a statistical analysis of the tracking lock conditions has been carried out leveraging a Monte Carlo approach to characterize the performance for different settings of the receiver front-end.
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