assessment of network-based positioning performance using gps alone versus gps and glonass combined

Assessment of Network-based Positioning

Performance Using GPS Alone versus GPS

and GLONASS Combined

Al-Shaery, A.1, Lim, S.

1, and Rizos, C.

1

1 School of Surveying and Spatial Information System, the University of New South Wales, Sydney, NSW, Australia

BIOGRAPHIES

Ali Al-Shaery is currently a doctoral student within the

Geodetic Infrastructure and Analysis (GIA) group in the

School of Surveying and Spatial Information Systems at

the University of New South Wales, Australia. He

obtained his B.Sc. degree in Civil Engineering from the

University of Umm Al-Qura, Saudi Arabia and M.Sc.

degree from University College London, United

Kingdom in 2003 and 2007, respectively. His current

research interests are mainly network-RTK algorithms

and applications.

Samsung Lim is an Associate Professor in the School of

Surveying and Spatial Information Systems, The

University of New South Wales (UNSW), Sydney,

Australia. Samsung's research focus is applying full

waveform lidar, GIS and GPS to geospatial and

environment problems. Samsung received his B.A. and

M.A. in Mathematics from Seoul National University

and his Ph.D. in Aerospace Engineering and Engineering

Mechanics from the University of Texas at Austin.

Chris Rizos is a graduate of the School of Surveying

and Spatial Information Systems, UNSW; obtaining a

Bachelor of Surveying in 1975, and a Doctor of

Philosophy in 1980. Chris is currently Professor and

Head of School. Chris has been researching the

technology and high precision applications of GPS since

1985, and has published over 400 journal and conference

papers. He is a Fellow of the Australian Institute of

Navigation and a Fellow of the International Association

of Geodesy (IAG). He is currently the President of the

IAG and a member of the Governing Board of the

International GNSS Service.

ABSTRACT

As it is anticipated that the full operational capability of

GLONASS will be achieved in the very near future,

GLONASS is now attracting surveyors’ attention, with

questions being asked on how much improved accuracy

can be obtained if GPS and GLONASS were used

together. Such a performance assessment has been

undertaken in the past; however, most of the tests were

conducted with a limited number of available (at the

time) GLONASS satellites. It is timely to re-assess the

performance because most networks of continuously

operating reference stations (CORS) are now equipped

with receivers that can track both GPS and GLONASS

satellites, and therefore network-based positioning with

combined GPS and GLONASS observations is possible.

This paper compares the network-based positioning

results with GPS measurements only versus the use of

combined GPS and GLONASS measurements, under

various sky view conditions.

The benefit of adding GLONASS measurements to GPS

measurements is more obvious when a limited number of

satellites are available due to the fact that sky view is

partially blocked. Comparing the GPS-only solution with

the GPS+GLONASS solution, the accuracy improves by

approximately 2mm and 3mm in the 2-dimensional and

3-dimensional coordinates, respectively. However, the

combined solution shows its clear advantage when

GLONASS-only solutions are considered.

INTRODUCTION

The performance, e.g. accuracy, availability and

reliability, of GPS is a function of the number of

satellites being tracked. Thus, the positioning function of

GPS is degraded in ‘urban canyon’ environments or in

deep open cut mines where the number of visible

satellites is limited. Adding more functioning satellites is

one of the aiding solutions.

Augmenting GPS satellite measurements with those

made on GLONASS would benefit high precise

positioning applications in both real-time and post-

mission modes, especially in areas where a limited

number of GPS satellites are visible. The inclusion of

GLONASS observations in positioning solutions will

increase the available number of satellites and thus

positioning accuracy may improve as a result of

enhanced overall satellite geometry. The GLONASS

constellation at the time of this study (2010) consisted of

21 operational satellites (IAC, 2010). Another

motivation is the availability of GLONASS final orbits

from the IGS and an individual analysis centre of the

IGS.

In this study a simulation was carried out to check the

number of GPS, GLONASS and GPS+GLONASS

satellites available for a user with three different levels

of sky view conditions. Firstly, the cut-off elevation

angle was set to 15 degrees, representing the case of

open sky view where no obstructions exist. Secondly, a

moderate cut-off elevation angle was set to 30degrees.

Thirdly, a severe condition was assumed where a limited

number of satellites were available assuming a high cut-

off elevation angle with 45 degrees .

The simulation results shown in Table 1 show the

percentage of visible time of a whole day (20/10/2010)

at a typical CORS where four or more satellites can be

tracked. The results show that augmenting the GPS

solution with GLONASS observations may not

significantly improve the quality compared to the GPS-

only solution under open sky view conditions. However,

the improvement can clearly be seen under severe

tracking conditions when very few GPS satellites are

visible.

With constellations of 29 and 21 active GPS and

GLONASS satellites, respectively, at the time of this

study, the number of combined observations will

increase by a factor of 1.7. Thus the corresponding

formal reduction error will be approximately 1.3. This

paper investigates the influence of combining

GLONASS and GPS data on network-based positioning

under different tracking site visibility conditions. As

GLONASS is close to being fully operational, the

objective of the study is not only to compare GPS-only

to GPS+GLONASS, but also to study the quality of

GLONASS-only to the combined GPS+GLONASS

solution.

Table 1: Percentage of time when four satellites or more

are available for different elevation cut-off angles.

cut-off angle 15 deg 30 deg 45 deg

GPS 99% 95% 28%

GLONASS 89% 51% 5%

GPS+GLONAS

S

99% 99% 91%

METHODOLOGY

The aim of the study is to compare GPS-only and

GLONASS-only to GPS+GLONASS scenarios, and to

therefore assess the influence of adding GLONASS

observations to a single-system solution under different

tracking conditions. The BERNESE software well

known scientific software capable of processing

GLONASS with GPS data was utilised in this study.

Using such software a network solution was computed to

obtain station coordinates with GPS or GLONASS

measurements in single-system mode (i.e. GPS-only and

GLONASS-only solutions), and on the other hand

GPS+GLONASS in the combined mode. The flow chart

given in Figure 1 indicates the procedure implemented in

this study to process both system data.

Figure 1: Processing flow used in this study.

PPP.PCF is the preparatory step before the double-

difference processing (RNX2SNX.PCF) where the

preliminary coordinates of the network stations used for

the observable linearisation process. In RNX2SNX.PCF

there are three main processes. The first step is to form

single-differenced observation files by selecting a set of

independent baselines between network stations using

several strategies. The strategy applied here was based

on the maximum number of observations formed for a

baseline. The second main process is to fix L1 and L2

ambiguity parameters using the Quasi-Ionosphere-free

(QIF) algorithm (Beutler et al., 2007). Determination of

final network station coordinates and troposphere

parameters is the third step.

In this study, Center for Orbit Determination in Europe

(CODE) orbit products were used because of its higher

accuracy (2.5cm for GPS and 5cm for GLONASS)

compared to the IGS products (5cm for GPS and 15cm

for GLONASS). The CODE analysis centre is one of the

two analysis centres producing truly multi-GNSS orbits,

meaning that GPS and GLONASS observations are

simultaneously used to generate combined

GPS+GLONASS orbits (Bruyninx, 2007, Springer and

Dach, 2010). The absolute antenna phase centre

variation model was applied in this study.

EXPERIMENT

Data used in the test were from seven stations of the

NSW state CORS network located in the Sydney region,

Australia (see Figure 2). The inter-station distances

range from 20.7km to 62.5km. All stations are equipped

a priori-coordinates

of stations (PPP.PCF)

independent

between-receiver

baselines

(RNX2SNX.PCF)

float-ambiguity

network solution

(RNX2SNX.PCF)

L1 and L2 ambiguity

resolution

(RNX2SNX.PCF)

Fixed-ambiguity

network solution

(RNX2SNX.PCF))

https://www.researchgate.net/publication/279546712_GPS_GLOIMASS_and_morei_multiple_constellation_processing_in_the_International_GNSS_Service?el=1_x_8&enrichId=rgreq-f6b1665130ecdf74b9b709299210f8ed-XXX&enrichSource=Y292ZXJQYWdlOzI2MzkzNzUxOTtBUzoxMjE1MTIzNjc0OTcyMTZAMTQwNTk4MjIxNDY0Mg==

with geodetic receivers capable of tracking both GPS

and GLONASS satellites. The receivers’ details are

given in Table 2. The study data set was two months in

length, from 1st of July till the end of August 2010.

Table 2: Receiver information in Sydney area of

CORSnet-NSW. StationStationStationStation

NameNameNameName

ReceiverReceiverReceiverReceiver TypeTypeTypeType Antenna + radomeAntenna + radomeAntenna + radomeAntenna + radome

CHIPCHIPCHIPCHIP LEICA

GRX1200GGPRO

ASH701945E_M SCIS

CWN2CWN2CWN2CWN2 TRIMBLE NETR5 ASH701945E_M SCIS

MENAMENAMENAMENA LEICA

GRX1200GGPRO

ASH701945E_M SCIS

MGRVMGRVMGRVMGRV LEICA

GRX1200GGPRO

ASH701945E_M SCIS

SPWDSPWDSPWDSPWD LEICA

GRX1200+GNSS

ASH701945E_M SCIS

VLWDVLWDVLWDVLWD TRIMBLE NETR5 ASH701945E_M SCIS

WFALWFALWFALWFAL TRIMBLE NETR5 ASH701945E_M SCIS

The Sydney network was used to assess the influence of

adding the GLONASS data to positioning solutions. The

quality of GLONASS-only solutions was also examined.

Firstly, initial estimates of network station coordinates

were obtained using the PPP processing control file

(PCF). Then the double-differenced baseline solution

was obtained using RNX2SNX.PCF (R2S) to estimate

network coordinates by only fixing GPS ambiguity

parameters. Final estimates of the coordinates are

expressed in the IGS05 reference frame (the IGS

realisation of the ITRF2005).

To achieve the goal of this study, three tests were carried

out (T5, T6 and T7), representing the three tracking

environment conditions (open, semi-open and limited

sky view). In the first test, 15° cut-off elevation angle

was applied, 30° in the second, and in the third it was

45°. Over the study period, the accuracy and the

repeatability of the estimated coordinates were

calculated and analysed in the Australia geodetic datum

(GDA94). Coordinates are estimated using three solution

scenarios: GPS-only, GLONASS-only and combined

GPS+GLONASS.

Figure 2: Sydney are portion of the CORSnet-NSW

network.

RESULTS AND ANALYSIS

Adding GLONASS data to GPS positioning solutions

not only increases the available satellites, which means

more observations, but also the number of unknown

parameters, which are the GLONASS ambiguities.

Compared to GPS-only or GLONASS-only, the

parameter increase is approximately similar to the

increased percentage of observations in the

GPS+GLONASS solution (see Figures 3-6).

Figure 3: Percentage increase in observations of GPS-

only against GPS+GLONASS solutions.

Figure 4: Percentage increase in ambiguity parameters of

GPS-only against GPS+GLONASS.

Figure 5: Percentage increase in observations of

GLONASS-only against GPS+GLONASS.

Figure 6: Percentage increase in ambiguity parameters of

GLONASS-only against GPS+GLONASS.

Some stations, for example at T5 (MENA and WFAL),

show very large errors (approximately 700mm in the

horizontal RMS and 900mm in the vertical component,

and approximately similar results for STDV (see Figures

7-8) as a result of being removed from the R2S solution

at sessions DOY-210 and DOY-215. Time series plots of

those stations show clearly the outliers. By investigating

the station statistics it was found that the stations’

corresponding files (DOY-210) were marked ‘bad’ and

were therefore deleted in the double-differenced

estimation. A station or baseline can be assumed

problematic and needs to be deleted from the solution if

its associated residuals exceed the user threshold

(Beutler et al., 2007). For DOY-210 this was caused by a

‘bad’ satellite PRN-16 at both stations. For DOY-215, all

stations except MENA and WFAL were identified as

‘bad’ stations. The solution of this session was

consequently terminated.

Figure 7: Accuracy comparison of T5 before outlier

removal between GPS-only, GLONASS-only and

GPS+GLONASS.

Figure 8: Repeatability comparison of T5 before outlier


GPS+GLONASS.

Figure 9: Time series plots of T5 local topocentric

coordinates of MENA before outlier removal for GPS-

only, GLONASS-only and GPS+GLONASS.


coordinates of WFAL before outlier removal for GPS-


Therefore a two-step outlier detection technique was

applied to remove these outliers in order to obtain

realistic accuracy and repeatability. The following plots

and statistic summary (Tables 3-5) were obtained.

Figure 11: Accuracy comparison of T5 after outlier


GPS+GLONASS.

Figure 12: Repeatability comparison of T5 after outlier


GPS+GLONASS.

Figure 11 compares the accuracy of both GPS and

GLONASS in single and combined modes for a low cut-

off elevation angle simulating the scenario where no

signal obstructions exist in the vicinity of the receiver. It

shows insignificant improvement from adding

GLONASS data to the GPS solution. This can be

explained because the GPS satellite geometry is already

good. Similar results were obtained for repeatability (see

Figure 14). Time series plots (see Figures 13-14) of these

two stations after outlier removal show their behaviour

over the test period. GLONASS-only solutions have

better accuracy in the north component but with higher

noise over all coordinate components compared to GPS-

only and GPS+GLONASS. For WFAL, no difference of

GLONASS accuracy was obtained whereas the higher

noise still exists. The higher noise can be explained by

the drop in the number of visible GLONASS satellites


coordinates of MENA after outlier removal for GPS-



coordinates of WFAL before outlier removal for GPS-


As the satellite elevation cut-off angle increases, the

accuracy and repeatability in general decrease regardless

of the satellite system utilised in a solution (see Tables 3-

5). This is an expected result as the number of available

satellites decreases which may degrade the satellite

geometry (see Figures 15-20). As can be seen, the

accuracy deteriorates significantly under limited sky

view conditions, when number of satellites drops to a

very low level.

Table 4: Statistics summary of T6 after outlier removal for GPS, GLONASS and GPS+GLONASS.

GPS GLONASS GPS+GLONASS

Hor(mm) Up(mm) 3-D Hor(mm) Up(mm) 3-D Hor(mm) Up(mm) 3-D

CHIP 7.316 19.325 20.663 14.871 18.539 23.766 8.949 20.514 22.381

CWN2 9.045 26.441 27.945 12.063 21.410 24.575 4.418 21.894 22.335

MGRV 7.936 8.533 11.653 7.491 8.162 11.079 5.623 7.513 9.384

SPWD 5.372 8.850 10.353 8.787 18.246 20.252 4.308 8.517 9.545

MENA 7.549 28.506 29.489 16.048 24.569 29.346 7.583 29.297 30.263

WFAL 7.476 6.960 10.214 18.201 13.841 22.866 12.359 8.465 14.980

VLWD 4.881 9.061 10.292 9.052 13.041 15.875 6.742 10.056 12.107

CHIP 5.498 8.383 10.025 13.183 10.143 16.633 3.225 8.108 8.726

CWN2 5.540 15.663 16.614 11.108 18.815 21.849 2.996 14.987 15.283

MGRV 2.844 8.246 8.723 5.468 8.217 9.870 2.417 7.355 7.742

SPWD 3.912 5.808 7.002 8.569 10.415 13.487 3.220 7.418 8.087

MENA 7.372 12.483 14.497 15.590 15.190 21.767 4.254 11.157 11.940

WFAL 5.431 6.098 8.166 17.029 13.712 21.864 4.108 7.791 8.808

VLWD 3.102 7.907 8.494 8.454 12.232 14.869 2.266 7.138 7.489



RMS Hor(mm) Up(mm) 3-D Hor(mm) Up(mm) 3-D Hor(mm) Up(mm) 3-D

CHIP 17.703 113.428 114.801 42.702 42.357 60.147 17.403 107.722 109.119

CWN2 14.954 61.101 62.904 39.082 72.484 82.349 9.592 50.589 51.490

MGRV 9.468 22.744 24.636 15.910 31.626 35.403 10.655 19.974 22.638

SPWD 13.098 26.511 29.570 26.196 30.415 40.141 8.035 17.296 19.071

MENA 22.784 71.980 75.500 46.273 74.255 87.493 18.883 66.144 68.787

WFAL 25.196 31.164 40.075 44.136 50.125 66.787 21.819 29.448 36.650

VLWD 15.189 32.626 35.988 25.942 67.944 72.728 15.327 47.065 49.498

STDv Hor(mm) Up(mm) 3-D Hor(mm) Up(mm) 3-D Hor(mm) Up(mm) 3-D

CHIP 11.427 28.831 31.013 37.918 35.625 52.028 9.418 24.980 26.696

CWN2 12.930 47.848 49.564 31.837 59.609 67.578 8.391 41.057 41.906

MGRV 5.937 22.706 23.469 14.472 30.965 34.180 3.870 16.077 16.536

SPWD 10.051 24.916 26.867 25.722 26.293 36.783 5.222 17.022 17.805

MENA 15.608 38.404 41.454 41.151 49.020 64.003 12.791 31.598 34.089

WFAL 16.296 30.358 34.455 41.875 43.349 60.271 12.331 26.413 29.150

VLWD 8.493 30.138 31.312 24.620 49.543 55.323 8.071 21.014 22.511



RMS Hor(mm) Up(mm) 3-D Hor(mm) Up(mm) 3-D Hor(mm) Up(mm) 3-D

CHIP 6.451 10.031 11.926 7.498 9.815 12.351 7.134 10.367 12.585

CWN2 3.538 12.229 12.730 5.377 15.361 16.275 3.551 12.469 12.965

MGRV 4.468 10.765 11.655 4.337 8.953 9.948 4.130 10.580 11.358

SPWD 4.730 10.087 11.141 4.673 13.459 14.247 4.346 9.884 10.797

MENA 6.292 10.891 12.578 2.870 10.769 11.145 4.295 12.186 12.921

WFAL 7.004 9.802 12.047 6.619 9.275 11.395 7.118 9.477 11.852

VLWD 4.716 10.295 11.324 3.914 8.977 9.793 4.750 9.786 10.878

STDv Hor(mm) Up(mm) 3-D Hor(mm) Up(mm) 3-D Hor(mm) Up(mm) 3-D

CHIP 2.230 6.301 6.684 2.703 7.154 7.648 1.994 6.242 6.553

CWN2 2.463 6.758 7.193 3.452 9.313 9.932 2.076 7.202 7.495

MGRV 1.411 6.114 6.275 1.740 6.079 6.323 1.325 5.967 6.112

SPWD 1.708 5.432 5.694 2.430 7.034 7.442 1.419 5.851 6.021

MENA 2.275 6.786 7.157 2.894 8.011 8.518 1.865 6.616 6.874

WFAL 2.329 6.817 7.204 3.015 7.347 7.942 1.868 6.095 6.375

VLWD 1.597 6.103 6.309 1.933 7.444 7.691 1.455 5.833 6.012

Chen et al. : Evaluation of EPOS-RT for real-time deformation monitoring

1

Figure 15: Accuracy comparison of GPS-only after

outlier removal for different sky view conditions.

Figure 16: Accuracy comparison of GPS+GLONASS

after outlier removal for different sky view conditions.

Figure 17: Accuracy comparison of GLONASS-only


Figure 18: Repeatability comparison of GPS-only after

outlier removal for different sky view conditions.

Figure 19: Repeatability comparison of

GPS+GLONASS after outlier removal for different sky

view conditions.

Figure 20: Repeatability comparison of GLONASS-only


As can be seen from the plots (Figures 21-24), there is

insignificant difference between GPS-only and

GPS+GLONASS solutions in the case of open sky

conditions, considering both 2-D and 3-D RMS values.

However, in some cases the GPS-only solution is better

than the combined solution.

Under very limited sky view conditions (45 degree cut-

off elevation angle), almost all stations show an

improvement in the 2-D and 3-D accuracy when

GLONASS augmented GPS in contrast to the open sky

view scenario when both GPS-only and GLONASS-only

are compared. More obvious improvement can be noted

for the GLONASS-only comparison: 1cm (3-D) and 2cm

(2-D). When the repeatability is considered it can be

clearly seen that augmenting GPS with GLONASS

measurements leads to a better solution (2-D or 3-D)

compared to a single-system solution, not only under

limited availability of satellites (mm-level and cm-level

compared to GPS-only and GLONASS only,

respectively) but also under open sky view conditions

(sub-mm compared to both single systems).

Figure 21: Horizontal accuracy comparison between

open and limited sky view for GPS-only, GLONASS-

only and GPS+GLONASS.

Figure 22: 3-D Accuracy comparison between open and

limited sky view for GPS-only, GLONASS-only and

GPS+GLONASS.

Figure 23: Horizontal precision comparison between

open and limited sky view for GPS-only, GLONASS-

only and GPS+GLONASS.

Figure 24: 3-D Precision comparison between open and

limited sky view for GPS-only, GLONASS-only and

GPS+GLONASS.

The following graphs (Figures 25-26) show coordinate

differences obtained from the introduction of GLONASS

observations into the solutions. Comparing the combined

GPS+GLONASS solution to the GPS-only solution, the

combined solution coordinates indicate in general better

coordinates, with about 5mm and 10mm horizontal and

vertical changes with maximum values occurring at the

highest cut-off elevation angles. Considering the

GLONASS-only solution, the combined solution

provides a better solution with maximum coordinate

differences of 30mm, which are clearly seen at the

highest cut-off elevation angle (45°), and few millimetre

changes when the low cut-off elevation angle (15°) is

applied.

Figure 25: Coordinate differences between GPS-only

and GPS+GLONASS after outlier removal for different

sky view conditions.

Figure 26: Coordinate differences between GLONASS-

only and GPS+GLONASS after outlier removal for

different sky view conditions.

CONCLUDING REMARKS

In this paper the authors report on a study to assess the

influence of augmenting GPS with GLONASS

measurements on positioning solutions under different

tracking conditions. The 7 CORS network is the Sydney

portion of the CORSnet-NSW, was used for this study.

Three tests were carried out to simulate different sky

view environments by applying different cut-off

elevation angles (15°, 30° and 45°). The BERNESE 5.0

software was used to process two months worth of

CORS data covering the period from beginning July to

end of August 2010.

From the test results the augmented (GPS+GLONASS)

solution compared to GPS-only solution has insignificant

improvement under open sky view conditions (with sub-

mm level changes in the 2-D and 3-D for accuracy and

precision). Same conclusions hold when GLONASS-

only is compared to the augmented solution.

The benefit of adding GLONASS data to GPS is more

obvious when a limited number of satellites are available

due to the sky view being partially blocked. Comparing

the GPS-only solution to the GPS+GLONASS solution,

the accuracy improves by approximately 2mm and 3mm

in the 2-D and 3-D, respectively. However, the

combined solution shows very clear advantage when

compared with the GLONASS-only solutions.

ACKNOWLEDGMENTS

The first author would like to thank the NSW

Department of Lands for the provision of the CORSnet-

NSW data. He is also grateful to the scholarship

provider, the Saudi Higher Education Ministry, and

especially the University of Umm Al-Qura.

REFERENCES

Beutler, G., H. Bock, E. Brockmann, R. Dach, P. Fridez,

W. Gurtner, H. Habrich, U. Hugentobler, D.

Ineichen, A. Jaeggi, M. Meindl, L. Mervart, M.

Rothacher, S. Schaer, R. Schmid, T. Springer,

P. Steigenberger, D. Svehla, D. Thaller, C.

Urschl & R. Weber (Eds). (2007) Bernese GPS

Software Version 5.0, Bern: Astronomical

Institute, University of Bern, Switzerland.

Bruyninx, C. (2007) Comparing GPS-only with

GPS + GLONASS positioning in a regional

permanent GNSS network. GPS Solutions,

11(2), 97-106.

IAC (2010), GLONASS constellation status,20-10-2010.

Accessed 20 October 2010,

<http://www.glonass-

ianc.rsa.ru/pls/htmldb/f?p=202:20:6037112934

71671::NO>.

Springer, T. & R. Dach (2010) GPS, GLONASS and

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assessment of network-based positioning performance using gps alone versus gps and glonass combined

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