generating vrs data using atmospheric models: how …€¦ · komjathy (1997) 4-order polynomial...
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unesp
Daniele Barroca Marra Alves (PhD Student)Adj. Prof. João Francisco Galera Monico
Luiz Fernando Antonio Dalbelo (MSc Student)Faculty of Science and Technology (FCT) - São Paulo State University (UNESP)
FCT/UNESP – Pres. Prudente, São Paulo, Brazil
Dr. Luiz Fernando SapucciCenter for Weather Forecasts and Climate Studies
CPTEC/INPE - São Jose dos Campos, São Paulo, Brazil
Generating VRS Data Using Generating VRS Data Using
Atmospheric Models: How Far Atmospheric Models: How Far Can We Go?Can We Go?
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OUTLINEOUTLINE
Introduction and Objectives
Atmospheric Models used
Ionosphere - Mod_Ion_FK
Troposphere - NWP
Network RTK – VRS concept
Methodology
Experiments and Analyses
Conclusions
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INTRODUCTIONINTRODUCTION
Nowadays, with the implantation of reference station networks, several
positioning techniques have been developed and/or improved.
Applying multiple reference station methods one can obtain higher
positioning accuracy in a larger coverage area
Reliability
Availability
Integrity
In addition to gain in:
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INTRODUCTIONINTRODUCTION
Several methods have been developed to generate corrections from network stations data
In this paper was decided to use the VRS concept, which may be quite useful in Brazil
VRS data are generated using a different methodology
In the proposed methodology ionospheric and tropospheric models developed
in Brazil were used
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IONOSPHEREIONOSPHERE At UNESP, a regional ionosphere model (Mod_Ion) has been developed
Presented good results in Point Positioning
INTRODUCTIONINTRODUCTION
TROPOSPHERETROPOSPHERE NWP model was used
The procedure used to compute the ZTD by NWP model was
jointly developed by UNESP and CPTEC/INPE
This kind of troposphere modeling has been very used by the scientific community
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MOD_ION MOD_ION –– Brief HistoricalBrief Historical
Mapping Function
Developed by
Camargo (1999)
Modeling Function
Standard Geometric Fourier Series
Added by
Matsuoka (2003)
Sardón et al. (1994)
Spherical Harmonic
Komjathy (1997)4-order polynomial
Taylor Series
Improvements by
Aguiar (2005)Kalman Filter
Fourier Modeling
Function was altered
Mod_Ion_FK Real time applications
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MOD_ION_FKMOD_ION_FK
Mod_Ion_FK has the goal
of providing ionosphere corrections in real time
The parameters of the model are estimated through the Kalman
Filter and the Gauss-Markov process for prediction
As a modeling function, a 19 coefficient Fourier series is used
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ZTD FROM NWPZTD FROM NWP
The use of ZTD prediction from NWP models is a good alternative to
minimize the effects of the troposphere for real time applications
It is based on the General Atmosphere Circulation Model of CPTEC/INPE
Zenithal tropospheric delay from NWP developed at
CPTEC/INPE & FCT/UNESP is available in Brazil
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ZTD FROM NWP ZTD FROM NWP –– CPTEC/INPECPTEC/INPE
The ZTD values are provided for all South America twice a day with predictions for a
period of 66 hours
Resolution: horizontal 100 x 100 km; vertical - 18 levels
It is under development: 20x20 km horizontal resolution; vertical - 19 levels
Available in: http://satelite.cptec.inpe.br/htmldocs/ztd/zenital.htm
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ZTD FROM NWP ZTD FROM NWP –– CPTEC/INPECPTEC/INPE
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ZTD FROM NWP ZTD FROM NWP –– CPTEC/INPECPTEC/INPE
0
35 cm
RESOLUTIONRESOLUTION
� horizontal: 20x20 km
� vertical: 19 levels
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NETWORK RTKNETWORK RTK
Using network RTK positioning, it is possible to model the distance dependent errors
Troposphere RefractionIonosphere Effect
PDA Interpolation
Conditional
AdjustmentVRS
Several methods have been developed to formulate corrections from a network stations data
A reference station close to the user
The VRS concept is quite useful in Brazil
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VRS CONCEPTVRS CONCEPT
The VRS data are not provided by a real receiver, but its data are generated from real GPS
observations collected by an active multiple reference station network
The user can accomplish the relative positioning using a single frequency
receiver
The user has the possibility of using the VRS as if it were a real reference
station in your proximities
Rover Stations
ReferenceStations
VRS
The idea is that the VRS data resemble
as much as possible a real
receiver data at the same location
Wanninger (1999)
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METHODOLOGYMETHODOLOGY
Yes
Input Data
IGS ephemeridesReference StationsCoordinates
VRS coordinates
End process
Use atmospheric corrections?
Base station choice and RINEX reading
Computing GC
No Generating theVRS file
Computing corrections
Generating theVRS file
Computing the atmospheric
effect differences
between the base and VRS stations
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In order to analyze the VRS performance it was accomplished the PPP, DGPS
and relative positioning with VRS data
It was used data from GPS Active Network of West of São Paulo State, and an extra station
EXPERIMENTSEXPERIMENTS
<http://gege.prudente.unesp.br>
The data were collected in 28, 29, 30 December 2006, 24 hours a day
The VRS was generated for one of the stations (PPTE)
The PPTE data were used just for testing
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EXPERIMENTSEXPERIMENTS
VRSVRS
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PPP PPP –– COORDINATES ANALYSESCOORDINATES ANALYSES
2.5120.372.80AverageAverage
3.2820.782.69002002
2.2021.092.51364364
2.3819.483.24363363
2.2020.112.77362362
GC+T+IGC+T+IGCGCPPTEPPTEDayDay
RMS (cm)
Static Mode
10.9539.2610.03AverageAverage
9.4448.4410.19002002
13.2737.9210.51364364
10.5634.5210.01363363
10.5436.169.40362362
GC+T+IGC+T+IGCGCPPTEPPTEDayDay
Kinematic Mode
RMS (cm)
NRCan software
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PPP PPP –– ZTD ANALYSESZTD ANALYSESStatic Mode
Kinematic ModeDay 362
Day 363
Day 363
Day 362
NRCan software
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DGPSDGPS
166.43161.05149.60AverageAverage
169.77165.25156.23364364
167.43161.88149.69363363
162.08156.02142.90362362
GC+T+IGC+T+IGCGCPPTEPPTEDayDay
RMS (cm)
VRS generated by GC and CG+T+I PPTE versus VRS
In house software – Dalbelo et al (2006)
Day 362
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RELATIVE POSITIONINGRELATIVE POSITIONING
1.6310.001.64AverageAverage
2.0110.821.96364364
1.609.891.46363363
1.289.281.51362362
GC+T+IGC+T+IGCGCPPTEPPTEDayDay
RMS (cm)
Static Mode
6.0823.186.20AverageAverage
6.2526.706.99364364
5.9321.485.64363363
6.0521.365.96362362
GC+T+IGC+T+IGCGCPPTEPPTEDayDay
Kinematic Mode
RMS (cm)
TGO software
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RELATIVE POSITIONING RELATIVE POSITIONING -- Kinematic Kinematic modemode
Day 362
Day 362
VRS generated by GC and CG+T+I
PPTE versus VRS
TGO software
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In this presentation it was showed the performance obtained by a VRS generated
using atmospheric models
IONOSPHERE – Mod_Ion_FK
TROPOSPHERE – NWP model
Developed by UNESP and CPTEC/INPE
The results obtained present evidences that the proposed methodology may
be quite efficient
The results provided by VRS are similar of those obtained by real data (PPTE)
CONCLUSIONSCONCLUSIONS
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FUTURE works
Test other ionospheric Models
Troposphere Model
This methodology was tested using other troposphere models (Hopfield
for example) – NWP provided the best results
CONCLUSIONSCONCLUSIONS
It has been developed a version concerning the ambiguity resolution
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Thank you for your attention!!!
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