University of New South Wales (UNSW), Sydney,. Australia. Samsung's research focus is applying full waveform lidar, GIS and GPS to geospatial and.
Assessment of Network-based Positioning Performance Using GPS Alone versus GPS and GLONASS Combined 1
Al-Shaery, A.1, Lim, S.1, and Rizos, C.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 postmission 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 cutoff elevation angle with 45degrees . 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 GPSonly 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 15deg 30deg 45deg 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.
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))
Figure 1: Processing flow used in this study. PPP.PCF is the preparatory step before the doubledifference 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
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. Station Name CHIP CWN2 MENA MGRV SPWD VLWD WFAL
Receiver Type
Antenna + radome
LEICA GRX1200GGPRO TRIMBLE NETR5 LEICA GRX1200GGPRO LEICA GRX1200GGPRO LEICA GRX1200+GNSS TRIMBLE NETR5 TRIMBLE NETR5
ASH701945E_M
SCIS
ASH701945E_M ASH701945E_M
SCIS SCIS
ASH701945E_M
SCIS
ASH701945E_M
SCIS
ASH701945E_M ASH701945E_M
SCIS 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 GPSonly 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 removal between GPS-only, GLONASS-only and GPS+GLONASS.
Figure 9: Time series plots of T5 local topocentric coordinates of MENA before outlier removal for GPSonly, GLONASS-only and GPS+GLONASS.
Figure 10: Time series plots of T5 local topocentric coordinates of WFAL before outlier removal for GPSonly, GLONASS-only and GPS+GLONASS. 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 removal between GPS-only, GLONASS-only and GPS+GLONASS.
Figure 12: Repeatability comparison of T5 after outlier removal between GPS-only, GLONASS-only and GPS+GLONASS. Figure 11 compares the accuracy of both GPS and GLONASS in single and combined modes for a low cutoff 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 GPSonly 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
Figure 13: Time series plots of T5 local topocentric coordinates of MENA after outlier removal for GPSonly, GLONASS-only and GPS+GLONASS.
Figure 14: Time series plots of T5 local topocentric coordinates of WFAL before outlier removal for GPSonly, GLONASS-only and GPS+GLONASS. 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 35). 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 3: Statistics summary of T5 after outlier removal for GPS, GLONASS and GPS+GLONASS. RMS CHIP CWN2 MGRV SPWD MENA WFAL VLWD STDv CHIP CWN2 MGRV SPWD MENA WFAL VLWD
GPS Hor(mm) 6.451 3.538 4.468 4.730 6.292 7.004 4.716 Hor(mm) 2.230 2.463 1.411 1.708 2.275 2.329 1.597
Up(mm) 10.031 12.229 10.765 10.087 10.891 9.802 10.295 Up(mm) 6.301 6.758 6.114 5.432 6.786 6.817 6.103
3-D 11.926 12.730 11.655 11.141 12.578 12.047 11.324 3-D 6.684 7.193 6.275 5.694 7.157 7.204 6.309
GLONASS Hor(mm) 7.498 5.377 4.337 4.673 2.870 6.619 3.914 Hor(mm) 2.703 3.452 1.740 2.430 2.894 3.015 1.933
Up(mm) 9.815 15.361 8.953 13.459 10.769 9.275 8.977 Up(mm) 7.154 9.313 6.079 7.034 8.011 7.347 7.444
3-D 12.351 16.275 9.948 14.247 11.145 11.395 9.793 3-D 7.648 9.932 6.323 7.442 8.518 7.942 7.691
GPS+GLONASS Hor(mm) Up(mm) 7.134 10.367 3.551 12.469 4.130 10.580 4.346 9.884 4.295 12.186 7.118 9.477 4.750 9.786 Hor(mm) Up(mm) 1.994 6.242 2.076 7.202 1.325 5.967 1.419 5.851 1.865 6.616 1.868 6.095 1.455 5.833
3-D 12.585 12.965 11.358 10.797 12.921 11.852 10.878 3-D 6.553 7.495 6.112 6.021 6.874 6.375 6.012
Table 4: Statistics summary of T6 after outlier removal for GPS, GLONASS and GPS+GLONASS. GPS Hor(mm)
GLONASS Up(mm)
3-D
Hor(mm)
GPS+GLONASS Up(mm)
3-D
Hor(mm)
Up(mm)
3-D
CHIP CWN2 MGRV SPWD MENA WFAL VLWD
7.316 9.045 7.936 5.372 7.549 7.476 4.881
19.325 26.441 8.533 8.850 28.506 6.960 9.061
20.663 27.945 11.653 10.353 29.489 10.214 10.292
14.871 12.063 7.491 8.787 16.048 18.201 9.052
18.539 21.410 8.162 18.246 24.569 13.841 13.041
23.766 24.575 11.079 20.252 29.346 22.866 15.875
8.949 4.418 5.623 4.308 7.583 12.359 6.742
20.514 21.894 7.513 8.517 29.297 8.465 10.056
22.381 22.335 9.384 9.545 30.263 14.980 12.107
CHIP CWN2 MGRV SPWD MENA WFAL VLWD
5.498 5.540 2.844 3.912 7.372 5.431 3.102
8.383 15.663 8.246 5.808 12.483 6.098 7.907
10.025 16.614 8.723 7.002 14.497 8.166 8.494
13.183 11.108 5.468 8.569 15.590 17.029 8.454
10.143 18.815 8.217 10.415 15.190 13.712 12.232
16.633 21.849 9.870 13.487 21.767 21.864 14.869
3.225 2.996 2.417 3.220 4.254 4.108 2.266
8.108 14.987 7.355 7.418 11.157 7.791 7.138
8.726 15.283 7.742 8.087 11.940 8.808 7.489
Table 5: Statistics summary of T7 after outlier removal for GPS, GLONASS and GPS+GLONASS. RMS CHIP CWN2 MGRV SPWD MENA WFAL VLWD STDv CHIP CWN2 MGRV SPWD MENA WFAL VLWD
GPS Hor(mm) 17.703 14.954 9.468 13.098 22.784 25.196 15.189 Hor(mm) 11.427 12.930 5.937 10.051 15.608 16.296 8.493
Up(mm) 113.428 61.101 22.744 26.511 71.980 31.164 32.626 Up(mm) 28.831 47.848 22.706 24.916 38.404 30.358 30.138
3-D 114.801 62.904 24.636 29.570 75.500 40.075 35.988 3-D 31.013 49.564 23.469 26.867 41.454 34.455 31.312
GLONASS Hor(mm) 42.702 39.082 15.910 26.196 46.273 44.136 25.942 Hor(mm) 37.918 31.837 14.472 25.722 41.151 41.875 24.620
Up(mm) 42.357 72.484 31.626 30.415 74.255 50.125 67.944 Up(mm) 35.625 59.609 30.965 26.293 49.020 43.349 49.543
3-D 60.147 82.349 35.403 40.141 87.493 66.787 72.728 3-D 52.028 67.578 34.180 36.783 64.003 60.271 55.323
GPS+GLONASS Hor(mm) Up(mm) 17.403 107.722 9.592 50.589 10.655 19.974 8.035 17.296 18.883 66.144 21.819 29.448 15.327 47.065 Hor(mm) Up(mm) 9.418 24.980 8.391 41.057 3.870 16.077 5.222 17.022 12.791 31.598 12.331 26.413 8.071 21.014
3-D 109.119 51.490 22.638 19.071 68.787 36.650 49.498 3-D 26.696 41.906 16.536 17.805 34.089 29.150 22.511
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 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 17: Accuracy comparison of GLONASS-only after outlier removal for different sky view conditions. Figure 20: Repeatability comparison of GLONASS-only after outlier removal for different sky view conditions. 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 cutoff 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, GLONASSonly 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, GLONASSonly 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.
From the test results the augmented (GPS+GLONASS) solution compared to GPS-only solution has insignificant improvement under open sky view conditions (with submm level changes in the 2-D and 3-D for accuracy and precision). Same conclusions hold when GLONASSonly is compared to the augmented solution.
Figure 25: Coordinate differences between GPS-only and GPS+GLONASS after outlier removal for different sky view conditions.
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 CORSnetNSW data. He is also grateful to the scholarship provider, the Saudi Higher Education Ministry, and especially the University of Umm Al-Qura. REFERENCES
Figure 26: Coordinate differences between GLONASSonly 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.
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