A Gravity Field Model Derived From the Short Dynamical Arcs of Champ

2007 ◽  
Vol 50 (1) ◽  
pp. 110-115 ◽  
Author(s):  
Xing-Fu ZHANG ◽  
Yun-Zhong SHEN ◽  
Lei-Ming HU
Keyword(s):  
Gravity Field ◽  
Field Model ◽  
Gravity Field Model

A 1.5km-resolution gravity field model of the Moon

2012 ◽  
Vol 329-330 ◽  
pp. 22-30 ◽  
Author(s):  
C. Hirt ◽  
W.E. Featherstone
Keyword(s):  
Gravity Field ◽  
Field Model ◽  
The Moon ◽  
Gravity Field Model

scholarly journals The combined global gravity field model XGM2019e

2020 ◽  
Vol 94 (7) ◽  
Author(s):  
P. Zingerle ◽  
R. Pail ◽  
T. Gruber ◽  
X. Oikonomidou
Keyword(s):  
Gravity Field ◽  
Field Model ◽  
Gravity Field Model ◽  
Global Gravity ◽  
Global Gravity Field Model

What can be expected from GNSS tracking of satellite constellations for temporal gravity field model determination?

2020 ◽  
Vol 222 (1) ◽  
pp. 661-677
Author(s):  
Hao Zhou ◽  
Zebing Zhou ◽  
Zhicai Luo ◽  
Kang Wang ◽  
Min Wei
Keyword(s):  
Gravity Field ◽  
Field Model ◽  
Geoid Height ◽  
Error Component ◽  
Expected Improvement ◽  
Gps Tracking ◽  
Satellite Constellations ◽  
Gravity Field Model ◽  
Gravity Field Determination ◽  
Temporal Gravity

SUMMARY The goal of this contribution is to investigate the expected improvement of temporal gravity field determination via a couple of high-low satellite-to-satellite tracking (HLSST) missions. The simulation system is firstly validated by determining monthly gravity field models within situ GRACE GPS tracking data. The general consistency between the retrieved solutions and those developed by other official agencies indicates the good performance of our software. A 5-yr full-scale simulation is then performed using the full error sources including all error components. Analysis of each error component indicates that orbit error is the main contributor to the overall HLSST-derived gravity field model error. The noise level of monthly solution is therefore expected to reduce 90 per cent in terms of RMSE over ocean when the orbit accuracy improves for a magnitude of one order. As for the current HLSST mission consisting of a current GNSS receiver and an accelerometer (10−10 and 10−9 m s–2 noise for sensitive and non-sensitive axes), it is expected to observe monthly (or weekly) gravity solution at the spatial resolution of about 1300 km (or 2000 km). As for satellite constellations, a significant improvement is expected by adding the second satellite with the inclination of 70° and the third satellite with the inclination of 50°. The noise reduction in terms of cumulative geoid height error is approximately 51 per cent (or 62 per cent) when the observations of two (or three) HLSST missions are used. Moreover, the accuracy of weekly solution is expected to improve 40–70 per cent (or 27–59 per cent) for three (or two) HLSST missions when compared to one HLSST mission. Due to the low financial costs, it is worthy to build a satellite constellation of HLSST missions to fill the possible gaps between the dedicated temporal gravity field detecting missions.

scholarly journals Same-beam VLBI observations of SELENE for improving lunar gravity field model

Radio Science ◽  
2010 ◽  
Vol 45 (2) ◽  
pp. n/a-n/a ◽  
Author(s):  
Q. Liu ◽  
F. Kikuchi ◽  
K. Matsumoto ◽  
S. Goossens ◽  
H. Hanada ◽  
...  
Keyword(s):  
Gravity Field ◽  
Field Model ◽  
Lunar Gravity ◽  
Gravity Field Model ◽  
Lunar Gravity Field ◽  
Vlbi Observations

Simulated experiment of recovering gravity field from the observations of satellite’s frequency signal

2020 ◽  
Author(s):  
Xinyu Xu ◽  
Ziyu Shen ◽  
Wenbin Shen ◽  
Yongqi Zhao
Keyword(s):  
Gravity Field ◽  
Field Model ◽  
Reference Model ◽  
Satellite Orbit ◽  
The Other ◽  
Frequency Signal ◽  
Gravity Field Model ◽  
Simulated Experiment ◽  
The One ◽  
Gravity Field Models

<p><span>Recovering the gravity field with the satellite’s frequency signal might be an alternative measuring mode in the future when the accuracy of the onboard clock was good enough. On the one hand, we analyze the performance of recovering gravity field model from the gravitational potentials with different accuracies on different satellite altitudes (from 200 km to 350 km) based on semi-analytical (SA) method. On the other hand, we analyze the performance based on the numerical analysis. First, the gravitational potentials along the satellite orbit are computed from the clock observations based on the method of satellite’s frequency signal with the accuracies of 10<sup>-16</sup> and 10<sup>-18</sup>s. Then, based on the derived gravitational potentials, we recovered the gravity field models up to degree and order 200 (corresponding to 100 km spatial resolution). At last, the errors of recovered models are validated by comparing with the reference model.</span></p>

A CHAMP‐only gravity field model from kinematic orbits using the energy integral

2003 ◽  
Vol 30 (20) ◽  
Author(s):  
Ch. Gerlach ◽  
L. Földvary ◽  
D. Švehla ◽  
Th. Gruber ◽  
M. Wermuth ◽  
...  
Keyword(s):  
Gravity Field ◽  
Field Model ◽  
Energy Integral ◽  
Gravity Field Model

Fitting the GPS/Leveling Quasi-Geoid Using Bayesian-Regulation BP Neural Network

2011 ◽  
Vol 90-93 ◽  
pp. 2903-2906
Author(s):  
Lei Song ◽  
Xiao Qing Hu
Keyword(s):  
Neural Network ◽  
Gravity Field ◽  
Bp Neural Network ◽  
Field Model ◽  
Gravity Field Model ◽  
Global Gravity ◽  
Fitting In ◽  
Gps Leveling ◽  
Global Gravity Field Model

The 2.5′×2.5′resolution local quasi-geoid is calculated using the global gravity field model and GPS/leveling data of region which points spacing is about 10km with the Bayesian- regulation BP neural network in this paper. The inner and outer precision of quasi-geoid are both superior 0.05m.The result indicat that the Bayesian regulation BP neural network could improve the precision of fitting and restrain the over-fitting in fitting. The region quasi-geoid excelled than 0.05m can be computed using the global gravity field model and about 10km baseline GPS/leveling data in smoothness region.

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