Attitude and orbit error in n-dimensional spaces

2006 ◽  
Vol 54 (3-4) ◽  
pp. 467-484 ◽  
Author(s):  
Daniele Mortari ◽  
Sante R. Scuro ◽  
Christian Bruccoleri
Keyword(s):  
Orbit Error

scholarly journals Influence of the GEO satellite orbit error fluctuation correction on the BDS WADS zone correction

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Binghao Wang ◽  
Jianhua Zhou ◽  
Bin Wang ◽  
Dianwei Cong ◽  
Hui Zhang
Keyword(s):  
Satellite Orbit ◽  
Orbit Error ◽  
Geo Satellite

scholarly journals Correction to: Orbit error characteristic and distribution of TLE using CHAMP orbit data

2020 ◽  
Vol 365 (7) ◽  
Author(s):  
Xiao-li Xu ◽  
Yong-qing Xiong
Keyword(s):  
Error Characteristic ◽  
Orbit Error ◽  
Orbit Data

scholarly journals Seasonal variation in the apparent height of the East Antarctic ice sheet

1997 ◽  
Vol 24 ◽  
pp. 191-198 ◽  
Author(s):  
D. Yi ◽  
C. R. Bentley ◽  
M. D. Stenoien
Keyword(s):  
Seasonal Variation ◽  
Accumulation Rate ◽  
Surface Elevation ◽  
Secular Change ◽  
Radar Signal ◽  
Real Surface ◽  
Radar Altimeter ◽  
Depth Of Penetration ◽  
Orbit Error ◽  
Satellite Radar

A satellite radar altimeter can be used to monitor surface elevation change over polar ice sheets. Thirty-five months of Geosat Exact Repeat Mission (ERM) data from November 1986 to September 1989 over a section of East Antarctica (69–72.1 ∘S, 80–140∘ E) have been used in this study. A model that considers both surface and volume scattering was used to retrack the altimeter waveforms. Surface elevations for each month after the first three were compared to the average elevations for the first 3 months through a crossover method. The averaged crossover elevation difference changed with time in a way that suggests a yearly cycle in surface elevation. The average amplitude of the cycle is about 0.6 m. We have been unable to find any satisfactory explanation for the observed changes, in terms of either sources of error or contributors to real surface-height changes. We strongly suspect that orbit error plays a major role in producing the variations, although we know of no quantitatively satisfactory source of a quasi-seasonal variation in orbit error. Other possible contributors include a real seasonal variation in accumulation rate, seasonal changes in the delay of the radar signal as it propagates through the atmosphere, unmodeled variations in the depth of penetration of the radar pulse into the firn, changes in the thickness of the ice and the firn zone in response to seasonal variations in pressure and temperature, and the inverted barometer effect. Even though we do not know the cause of the variations, the results show the importance of comparing elevations at the same time of year for observations that are not continuous, while at the same time showing that even annually spaced measurements may not be free of substantial errors associated with interannual variability. The quasi-periodic variations obscure any evidence of a moderate secular change in surface height, if there is one, but a dramatic lowering at rates approaching 1 ma–1, such as are known elsewhere in Antarctica, can definitely be ruled out.

scholarly journals Initial Results of Modeling and Improvement of BDS-2/GPS Broadcast Ephemeris Satellite Orbit Based on BP and PSO-BP Neural Networks

2021 ◽  
Vol 13 (23) ◽  
pp. 4801
Author(s):  
Hanlin Chen ◽  
Fei Niu ◽  
Xing Su ◽  
Tao Geng ◽  
Zhimin Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Bp Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Orbit Error ◽  
Broadcast Ephemeris ◽  
Bp Neural Network Model ◽  
Orbit Errors

With the rapid development and gradual perfection of GNSS in recent years, improving the real-time service performance of GNSS has become a research hotspot. In GNSS single-point positioning, broadcast ephemeris is used to provide a space–time reference. However, the orbit parameters of broadcast ephemeris have meter-level errors, and no mathematical model can simulate the variation of this, which restricts the real-time positioning accuracy of GNSS. Based on this research background, this paper uses a BP (Back Propagation) neural network and a PSO (Particle Swarm Optimization)–BP neural network to model the variation in the orbit error of GPS and BDS broadcast ephemeris to improve the accuracy of broadcast ephemeris. The experimental results showed that the two neural network models in GPS can model the broadcast ephemeris orbit errors, and the results of the two models were roughly the same. The one-day and three-day improvement rates of RMS(3D) were 30–50%, but the PSO–BP neural network model was better able to model the trend of errors and effectively improve the broadcast ephemeris orbit accuracy. In BDS, both of the neural network models were able to model the broadcast ephemeris orbit errors; however, the PSO–BP neural network model results were better than those of the BP neural network. In the GEO satellite outcome of the PSO–BP neural network, the STD and RMS of the orbit error in three directions were reduced by 20–70%, with a 20–30% improvement over the BP neural network results. The IGSO satellite results showed that the PSO–BP neural network model output accuracy of the along- and radial-track directions experienced a 70–80% improvement in one and three days. The one- and three-day RMS(3D) of the MEO satellites showed that the PSO–BP neural network has a greater ability to resist gross errors than that of the BP neural network for modeling the changing trend of the broadcast ephemeris orbit errors. These results demonstrate that using neural networks to model the orbit error of broadcast ephemeris is of great significance to improving the orbit accuracy of broadcast ephemeris.

scholarly journals Addressing Angular Single-Event Effects in the Estimation of On-Orbit Error Rates

2015 ◽  
Vol 62 (6) ◽  
pp. 2563-2569 ◽  
Author(s):  
David S. Lee ◽  
Gary M. Swift ◽  
Michael J. Wirthlin ◽  
Jeffrey Draper
Keyword(s):  
Error Rates ◽  
Single Event ◽  
Orbit Error ◽  
Single Event Effects

scholarly journals Application of satellite altimeter data to orbit error correction and gravity model adjustment

1990 ◽  
Vol 10 (3-4) ◽  
pp. 249-267 ◽  
Author(s):  
R.C.A. Zandbergen ◽  
K.F. Wakker ◽  
B.A.C. Ambrosius
Keyword(s):  
Error Correction ◽  
Gravity Model ◽  
Altimeter Data ◽  
Orbit Error ◽  
Satellite Altimeter ◽  
Model Adjustment ◽  
Satellite Altimeter Data

Radial orbit error reduction and mean sea surface computation from the Geosat altimeter data

1994 ◽  
Vol 99 (B3) ◽  
pp. 4519-4531 ◽  
Author(s):  
S. Houry ◽  
J. F. Minster ◽  
C. Brossier ◽  
K. Dominh ◽  
M. C. Gennero ◽  
...  
Keyword(s):  
Error Reduction ◽  
Sea Surface ◽  
Altimeter Data ◽  
Orbit Error ◽  
Mean Sea Surface ◽  
Radial Orbit ◽  
Radial Orbit Error

Geographically correlated orbit error and its effect on satellite altimetry missions

1985 ◽  
Vol 90 (C6) ◽  
pp. 11817 ◽  
Author(s):  
B. D. Tapley ◽  
G. W. Rosborough
Keyword(s):  
Satellite Altimetry ◽  
Orbit Error

Orbit Determination of Geostationary Earth Orbit Satellite by Transfer with Differenced Ranges between Slave-Slave Stations

2013 ◽  
Vol 67 (1) ◽  
pp. 163-175 ◽  
Author(s):  
Cao Fen ◽  
Yang XuHai ◽  
Su MuDan ◽  
Li ZhiGang ◽  
Feng ChuGang ◽  
...  
Keyword(s):  
Orbit Determination ◽  
Very Long Baseline Interferometry ◽  
Baseline Length ◽  
Positioning System ◽  
Orbit Error ◽  
Long Baseline ◽  
Data Volume ◽  
Clock Offset ◽  
Range Observation

In order to more restrict the transverse orbit error, a new method named “differenced ranges between slave stations by transfer”, similar to Very Long Baseline Interferometry (VLBI) observation, has been developed in the Chinese Area Positioning System (CAPS). This method has the number of baselines added, the baseline length increased and the data volume enlarged. In this article, the principle of “differenced ranges between slave stations by transfer” has been described in detail, with the clock offset between slave stations and system error which affects the precision of the differenced ranges observation being discussed. Using this method, the differenced observation of the SINOSAT-1 satellite with C-band between slave stations from 6 to 13 June 2005 was conducted. Then a comparison was made between the accuracy of orbit determination and orbit prediction. A conclusion can be drawn that the combination of pseudo-range receiving the own-station-disseminated signal and the differenced range observation between slave-slave stations has a higher orbit determination and prediction accuracy than using only the former.

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