Title
Analysis of Orbital Atmospheric Density from QQ-Satellite Precision Orbits Based on GNSS Observations
Abstract
Atmospheric drag provides an indirect approach for evaluating atmospheric mass density, which can be derived from the Precise Orbit Determination (POD) of Low Earth Orbit (LEO) satellites. A method was developed to estimate nongravitational acceleration, which includes the drag acceleration of the thermospheric density model and empirical force acceleration in the velocity direction from the centimeter-level reduced-dynamic POD. The main research achievements include the study of atmospheric responses to geomagnetic storms, especially after the launch of the spherical Qiu Qiu (QQ)-Satellite (QQ-Satellite) with the global navigation system satellite (GNSS) receiver onboard tracking the Global Positioning System (GPS) and Beidou System (BDS) data. Using this derivation method, the high-accuracy POD atmospheric density was determined from these data, resulting in better agreement among the QQ-Satellite-derived densities and the NRLMSISE-00 model densities. In addition, the POD-derived density exhibited a more sensitive response to magnetic storms. Improved accuracy of short-term orbit predictions using derived density was one of the aims of this study. Preliminary experiments using densities derived from the QQ-Satellite showed promising and encouraging results in reducing orbit propagation errors within 24 h, especially during periods of geomagnetic activity.
Year
DOI
Venue
2022
10.3390/rs14163873
REMOTE SENSING
Keywords
DocType
Volume
QQ-Satellite, atmospheric mass density, precise orbit, drag acceleration, propagation
Journal
14
Issue
ISSN
Citations 
16
2072-4292
0
PageRank 
References 
Authors
0.34
0
16
Name
Order
Citations
PageRank
Yueqiang Sun101.01
Bowen Wang200.34
Xiangguang Meng300.34
Xinchun Tang400.34
Feng Yan500.34
Xianguo Zhang600.34
Weihua Bai701.69
Qifei Du805.07
Xianyi Wang900.68
Yuerong Cai1000.68
Bibo Guo1100.34
Shilong Wei1200.34
Hao Qiao1312.09
Peng Hu1400.68
Y. P. Li159212.55
Xinyue Wang1600.34