Suppr超能文献

利用巨型星座低地球轨道卫星进行定位的拟议轨道产品。

Proposed Orbital Products for Positioning Using Mega-Constellation LEO Satellites.

作者信息

Wang Kan, El-Mowafy Ahmed

机构信息

School of Earth and Planetary Sciences, Curtin University, Perth 6102, Australia.

出版信息

Sensors (Basel). 2020 Oct 14;20(20):5806. doi: 10.3390/s20205806.

Abstract

With thousands of low Earth orbit (LEO) satellites to be launched in the near future, LEO mega-constellations are supposed to significantly change the positioning and navigation service for ground users. The goal of this contribution is to suggest and discuss the feasibility of possible procedures to generate the LEO orbital products at two accuracy levels to facilitate different positioning methods-i.e., Level A orbits with meter-level accuracy as LEO-specific broadcast ephemeris, and Level B orbits with an accuracy of centimeters as polynomial corrections based on Level A orbits. Real data of the LEO satellite GRACE FO-1 are used for analyzing the error budgets. For the Level A products, compared to the orbital user range errors (OUREs) of a few centimeters introduced by the ephemeris fitting, it was found that the orbital prediction errors play the dominant role in the total error budget-i.e., at around 0.1, 0.2 and 1 m for prediction intervals of 1, 2 and 6 h, respectively. For the Level B products, the predicted orbits within a short period of up to 60 s have an OURE of a few centimeters, while the polynomial fitting OUREs can be reduced by a few millimeters when increasing the polynomial degree from one to two.

摘要

随着数千颗近地轨道(LEO)卫星将在不久的将来发射,LEO巨型星座预计将显著改变地面用户的定位和导航服务。本文的目的是提出并讨论以两种精度级别生成LEO轨道产品的可能程序的可行性,以促进不同的定位方法,即作为LEO特定广播星历的具有米级精度的A级轨道,以及基于A级轨道作为多项式校正的具有厘米级精度的B级轨道。利用LEO卫星GRACE FO-1的实际数据来分析误差预算。对于A级产品,与星历拟合引入的几厘米的轨道用户距离误差(OURE)相比,发现轨道预测误差在总误差预算中起主导作用,即在1、2和6小时的预测间隔分别约为0.1、0.2和1米。对于B级产品,在长达60秒的短时间内预测轨道的OURE为几厘米,而当多项式次数从1增加到2时,多项式拟合OURE可减少几毫米。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a168/7602220/075fc9e6987e/sensors-20-05806-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验