A smooth and robust Harris-Priester atmospheric density model for low Earth orbit applications

2017 ◽  
Vol 59 (2) ◽  
pp. 571-586 ◽  
Author(s):  
Noble Hatten ◽  
Ryan P. Russell
2021 ◽  
Author(s):  
Shunchenqiao Bai ◽  
Guangwei Wen ◽  
Zhaokui Wang

Abstract Atmospheric drag calculation error greatly reduce the low-earth orbit spacecraft trajectory prediction fidelity. To solve the issue, the "correction - prediction" strategy is usually employed. In the method, one parameter is fixed and other parameters are revised by inverting spacecraft orbit data. However, based on a single spacecraft data, the strategy usually performs poorly as parameters in drag force calculation are coupled with each other, which result in convoluted errors. A gravity field recovery and atmospheric density detection satellite, Q-Sat, developed by xxxxx Lab at xxx University, is launched on August 6th, 2020. The satellite is designed to be spherical for a constant drag coefficient regardless of its attitude. An orbit prediction method for low-earth orbit spacecraft with employment of Q-Sat data is proposed in present paper for decoupling atmospheric density and drag coefficient identification process. For the first step, by using a dynamic approach-based inversion, several empirical atmospheric density models are revised based on Q-Sat orbit data. Depends on the performs, one of the revised atmospheric density model would be selected for the next step in which the same inversion is employed for drag coefficient identification for a low-earth orbit operating spacecraft whose orbit needs to be predicted. Finally, orbit forecast is conducted by extrapolation with the dynamic parameters in the previous steps. Tests are carried out for the proposed method by using a GOCE satellite 15-day continuous orbit data. Compared with legacy “correction - prediction” method in which only GOCE data is employed, the accuracy of the 24-hour orbit prediction is improved by about 171m the highest for the proposed method. 14-day averaged 24-hour prediction precision is elevated by approximately 70m.


1998 ◽  
Vol 46 (4) ◽  
pp. 395-409 ◽  
Author(s):  
F. A. Marcos ◽  
M. J. Rendra ◽  
J. M. Griffin ◽  
J. N. Bass ◽  
D. R. Larson ◽  
...  

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Zeyang Zhao ◽  
Zhaokui Wang ◽  
Yulin Zhang

A spherical microsatellite, aimed at technology verification of upper atmospheric density detection and precision orbit determination in low Earth orbit (LEO), has been developed by Tsinghua University and is scheduled to launch in the second half of 2019. In order to reduce the influence of other nonconservative forces, the area-to-mass ratio of the satellite was designed to ensure that the aerodynamic drag of the satellite is more than one to two orders of magnitude greater than the solar pressure perturbation. The influence of the components of the antenna, separation support, and solar cell arrays (SCAs) on the spherical structure for the area-to-mass ratio is reduced by reasonable design of the satellite flight attitude. In addition, a novel method has been developed to estimate and correct the parameters for atmospheric density (correlated to the drag) calculation, using precise orbit data (POD) obtained from an onboard dual-frequency global position system (GPS) receiver. The method is used to obtain the partial derivative of the total acceleration of the satellite to the initial state (position, velocity) and Harris-Priester (HP) density model parameters (antapex and apex density). Further, the least squares estimation method is used to solve the overdetermined linear equations and obtain the change of initial state of the satellite and the HP density model parameters relative to change of the satellite state. The validity of this method has been verified through numerical simulations with the parametric setup of the satellite. The estimated density precision was found to be higher than two to three orders of the initial HP density model with continuous iteration.


1983 ◽  
Author(s):  
I. KATZ ◽  
D. COOKE ◽  
D. PARKS ◽  
M. MANDELL ◽  
A. RUBIN

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