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Online PPP service comparison using GNSS data from the global IGS stations
network
Rafael CoutoC. Barrico, R.M.S. Fernandes, M.S. Bos
SEGAL (UBI/IDL), Covilhã, Portugal
9ª AHPGG, 28-30 June 2016 - Madrid, Spain
Outline
• Motivation;
• Sample data used;
• Methodology;
• Relation between online PPP services;
• Scaling procedures;
• Conclusions.
9ª AHPGG, 28-30 June 2016 - Madrid, Spain
Main Motivation
• Improvement of merging techniques for solutions computed using online PPP processing services.
• How to obtain the improved position by weighted averaging the solutions from such services.
9ª AHPGG, 28-30 June 2016 - Madrid, Spain
= Improved Positioning+CSRS - PPPGDGPS - APPS
+AUSPOS
Stations Network – Distribution
9ª AHPGG, 28-30 June 2016 - Madrid, Spain
A set of 51 stations from the
IGS network were chosen
and all available data for 2015
was considered.
Stations Network – Data available
9ª AHPGG, 28-30 June 2016 - Madrid, Spain
A set of 51 stations from the IGS network were chosen and all available data for 2015 was considered.
Average number of RINEX daily files: 358 files (98%)
0
10
20
30
40
50
60
70
80
90
100
ALIC
ALRT
ARTU
AUCK
BJFS
BRAZ
BRM
UCA
S1CH
URDU
BOFA
IRGL
PSGO
DEGU
UGHL
FXHO
B2HO
LBII
SCKE
LYKE
RGKO
KBKO
UCLP
GSM
AC1
MAN
AM
ATE
MCM
4NK
LGNL
IBNR
ILNY
A1OH
I3PA
RCPD
ELPI
E1PO
LVPO
TSQU
INRE
UNSC
H2SF
ERST
JOSY
OGTH
TITI
XITL
SETR
O1TS
KBW
IND
YARR
YIBL
PERCENTAGE OF AVAILABLE RINEX FILES FOR EACH STATION FOR 2015
Methodology
1. Process all available RINEX files using the PPP online services;
2. Perform DIRECT weighted mean from STACOV files returned from online services.
9ª AHPGG, 28-30 June 2016 - Madrid, Spain
GDGPS - APPS
CSRS - PPP
Process all RINEX files using online PPP services
9ª AHPGG, 28-30 June 2016 - Madrid, Spain
= Improved Positioning
+CSRS - PPPGDGPS - APPS
+AUSPOS
0102030405060708090
100
ALIC
ALRT
ARTU
AUCK
BJFS
BRAZ
BRM
UCA
S1CH
URDU
BOFA
IRGL
PSGO
DEGU
UGHL
FXHO
B2HO
LBII
SCKE
LYKE
RGKO
KBKO
UCLP
GSM
AC1
MAN
AM
ATE
MCM
4NK
LGNL
IBNR
ILNY
A1OH
I3PA
RCPD
ELPI
E1PO
LVPO
TSQU
INRE
UNSC
H2SF
ERST
JOSY
OGTH
TITI
XITL
SETR
O1TS
KBW
IND
YARR
YIBL
PERCENTAGE OF AVAILABLE PROCESSED RINEX FILES FOR EACH ONLINE PPP SERVICE
NRCAN GDGPS AUSPOS
Average processed RINEX files: GDGPS-APPS - 95%, CSRS-PPP - 100%, AUSPOS – 4%
Methodology
1. Process all available RINEX files using the PPP online services;
2. Perform DIRECT weighted mean from STACOV files returned from online services.
9ª AHPGG, 28-30 June 2016 - Madrid, Spain
GDGPS - APPS
CSRS - PPP
Quick theory on formal errors
Formal errors are too optimistic since not all error sources are taken into account.
Examples of such missing error sources are: GNSS satellite orbit errors, multi-path,ocean tide loading errors. So, what comes out of the GNSS software analysissoftware is just a formal error of the position which does not take all physical errorsources into account (Santamaria-Gómez et al., 2011). Therefore, the best we cando is to increase the errors of the observations to better reflect the influence of thephysical sources.
9ª AHPGG, 28-30 June 2016 - Madrid, Spain
Methodology – Refined approach
1. Study relation between formal errors given by the online PPP services;• Compute the ratio between formal errors given by the 2 services
2. Process solutions of each service with HECTOR and study results;• Compute the WRMS of the residuals between daily positions and
trend (i.e., misfit all over the year) for each service and compute the ratio between them.
3. Develop method to correct formal errors of solutions in order to provide a proper relative weighted mean using the two above ratios.
4. Apply developed method to solutions.
9ª AHPGG, 28-30 June 2016 - Madrid, Spain
Relation between Errors of Online PPP Services
For each station the 3D ratio between formal errors was determined.
The average ratio is 5.61 (scale factor for GDGPS-APPS files).
9ª AHPGG, 28-30 June 2016 - Madrid, Spain
0
1
2
3
4
5
6
7
8
9
10
MAT
EN
YA1
CAS1
TIXI
BRAZ
LPG
SSY
OG
TRO
1D
UBO
NLI
BSF
ERCH
UR
OH
I3H
LFX
SCH
2PO
TSST
JORE
UN
BJFS
THTI
MAC
1AL
ICBR
MU
PDEL
WIN
DAU
CKPO
LVH
OLB
TLSE
HO
B2YA
RRPI
E1KO
UC
MCM
4N
KLG
GU
UGKE
RG IISC
FAIR
GLP
SKE
LYQ
UIN
KOKB
GO
DE
ARTU
MAN
ATS
KBN
RIL
ALRT
PARC
YIBL
Average ratio in 3D between CSRS-PPP and GDGPS-APPS
Hector – Time Series Analysis (http://segal.ubi.pt/hector)
Simultaneously Computation of:
• Exponential / Logarithmic Post-relaxation;
• Secular Trend;
• Seasonal Signals;
• Offsets;
• Power-law errors;
• Spectrum Index.
9ª AHPGG, 28-30 June 2016 - Madrid, Spain
Processing Data with Hector
9ª AHPGG, 28-30 June 2016 - Madrid, Spain
The variation around themean position is morerealistic estimate of theerror.
Hector outputs a set ofdata including theresiduals obtainedbetween a calculatedfitted/optimal solution andthe observed data.
Processing Data with Hector - 2
Using the average ratio between both services, for the horizontalcomponents and the vertical component independently, it was possible tofind reasonable scale factors between the services.
Since Hector outputs results using local system (ENU) and STACOV filesare in Cartesian system (XYZ), coordinate transformation is needed in orderto achieve independent scaling operations between horizontal and verticalcomponents.
9ª AHPGG, 28-30 June 2016 - Madrid, Spain
Service Horizontal Vertical
GDGPS - APPS 2.73mm 6.69mm
CSRS - PPP 3.26mm 5.15mm
Average RMS of residuals for both services
Service Horizontal Vertical
GDGPS − APPSCSRS − PPP 0.8354 1.2985
CSRS − PPPGDGPS − APPS 1.1970 0.7701
Ratio between average RMS for both services (Scale Factor)
Scaling operations – Procedure
• Scale up GDGPS STACOV files in order to keep formal errors conservative;
• Apply coordinate transformation equations in order to transform Cartesian to Local and apply component independent weights;
• Reapply coordinate transformation equations to transform Local to Cartesian again.
9ª AHPGG, 28-30 June 2016 - Madrid, Spain
Scaling operations
1. Calculation of variance-covariance matrix in Local coordinates:
2. Calculation of scaled variance-covariance matrix in Local coordinates:
3. Calculation of scaled variance-covariance matrix in Cartesian coordinates:
4. Extraction of standard-deviation and correlation matrices derived from scaledvariance-covariance matrix:
9ª AHPGG, 28-30 June 2016 - Madrid, Spain
Scaling operations – Final STACOV files
• Before
• After
9ª AHPGG, 28-30 June 2016 - Madrid, Spain
GDGPS - APPS
CSRS - PPP
GDGPS - APPS
CSRS - PPP
Conclusions
The developed method allows to improve the weighted average of individual onlinePPP services.
It takes into account:• The ratio between the formal errors, computed using a global distributed network
of 51 IGS stations;• The relative accuracy between services, which can be different in the horizontal
and vertical components (which is also taken into account).
This method was implemented for two services: GDGPS and NRCAN. In the nearfuture, we will also include the AUSPOS solutions using the same methodology.
9ª AHPGG, 28-30 June 2016 - Madrid, Spain
Acknowledgments
• National Resources of Canada – CSRS–PPP• Mário Bérubé
• JPL/NASA – GDGPS-APPS• Robert Khachikyan
• Australian Government – Geoscience Australia
• Project SOGRA (supported by EOARD – US Air Force)
9ª AHPGG, 28-30 June 2016 - Madrid, Spain
Thank you!
For nice comments: [email protected]
For nasty comments and bad remarks:[email protected]@[email protected]
Rafael Couto,C. Barrico, R.M.S. Fernandes, M.S. Bos.
SEGAL (UBI/IDL), Covilhã - Portugal
9ª AHPGG, 28-30 June 2016 - Madrid, Spain
