论文标题
一种基于Python的工具，用于从DSN的闭环档案跟踪数据文件中构造可观测值
A Python-based tool for constructing observables from the DSN's closed-loop archival tracking data files
论文作者
论文摘要
从NASA的深空网络（DSN）收集的无线电科学数据以各种格式通过NASA的行星数据系统（PDS）提供。这些数据中的大多数都以复杂的格式包装，使用户无法访问没有专门知识。在本文中，我们提出了一个基于Python的工具，该工具可以预处理闭环档案跟踪数据文件（ATDFS），生产多普勒和范围可观察物，并将它们与辅助信息一起写入ASCII表中。 ATDF是原始的闭环无线电科学产品，可用文档有限。在2000年代初，DSN弃用了ATDF，并将其替换为跟踪和导航服务数据文件（TNF），以跟上无线电科学系统的发展。大多数数据处理软件（例如，轨道确定软件）无法直接使用它们，从而限制了这些数据的利用。因此，尚未使用现代软件来处理绝大多数历史闭环无线电科学数据，并且我们对太阳系的理解得到了提高。本文提出的预处理工具使得可以使用现代技术和软件进行重新访问此类历史数据，以进行关键的无线电科学实验。
Radio science data collected from NASA's Deep Space Networks (DSNs) are made available in various formats through NASA's Planetary Data System (PDS). The majority of these data are packed in complex formats, making them inaccessible to users without specialized knowledge. In this paper, we present a Python-based tool that can preprocess the closed-loop archival tracking data files (ATDFs), produce Doppler and range observables, and write them in an ASCII table along with ancillary information. ATDFs are primitive closed-loop radio science products with limited available documentation. Early in the 2000s, DSN deprecated ATDF and replaced it with the Tracking and Navigation Service Data Files (TNF) to keep up with the evolution of the radio science system. Most data processing software (e.g., orbit determination software) cannot use them directly, thus limiting the utilization of these data. As such, the vast majority of historical closed-loop radio science data have not yet been processed with modern software and with our improved understanding of the solar system. The preprocessing tool presented in this paper makes it possible to revisit such historical data using modern techniques and software to conduct crucial radio science experiments.
