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The main purpose of this study is to develop a new Windowsbased program that calculates a quality control parameter that shows the quality of Global Positioning System (GPS) observations using GPS data in a Receiver INdependent Exchange (RINEX) format. This new program, Global Positioning System Quality Control (GPSQC), allows general GPS users to easily and intuitively check the quality of GPS observations before postprocessing, which will lead to the improvement of GPS positioning precision in diverse areas of GPS applications. The GPSQC is designed to control the multipath, cycle slip, and ionospheric errors of L_{1} and L_{2} signals in GPS observations. The GPSQC was developed using C#.NET language for the Window series with Microsoft Graphical User Interfaces (MS GUIs). This program gives brief information for GPS observations, time series plots, graphs of quality control parameters, and a summary report in MS word, Excel and PDF formats. It can simply perform quality checking of GPS observations that is difficult for surveyors conducting field work. We expect that GPSQC can be used to improve the accuracy of positioning and to solve timeconsuming problems due to data loss and large errors in GPS observations.
The quality of GPS positioning is related to a number of error factors [
To obtain consistent highprecision positioning results with the GPS carrierphase analysis, errors that are not specified in a functional or stochastic model must be detected correctly, removed and controlled in data processing. Reliability, which refers to the ability to detect such errors and to estimate the effects that they may have on a solution, is one of the main issues in GPS data quality control. A comprehensive investigation of quality issues in GPS observations was performed by the Special Study Group (SSG) 1.154 of the International Association of Geodesy (IAG) between 1996 and 1999 [
Two quality control programs, Translation, Editing and Quality Checking (TEQC) and GPS Qualtiy Control (GQC), are now available for GPS observations. The conceptual and theoretical background of TEQC was originally written by Chris Rocken [
GQC was developed as part of a Ph.D. dissertation [
However, neither of these programs is userfriendly because TEQC and GQC executables are not available on MS Windows GUIs; they are all command line programs. Therefore, the quality control results of GPS observations provided by TEQC and GQC are not easily understood by general GPS users, other than experts, and, in the general, nonacademic parts of GPS applications, the data quality check with these programs are often ignored or omitted. Accordingly, the desired precision level is not achieved even though the GPS sensing data observed from field work are postprocessed precisely. In this case, GPS observations must be repeated until the desired precision level is achieved. This process is significantly timeconsuming and costly.
The main purpose of this study is to develop a Windowsbased program that calculates the quality control parameter that shows the quality of GPS observations using the GPS sensing data in a RINEX format. This allows general GPS users to easily and intuitively check the quality of GPS observations before postprocessing, which will lead to the improvement of GPS positioning precision in diverse areas of GPS applications. In addition, by directly determining the observation adequacy and reobservation simultaneously during observation, the expense in terms of time and cost due to the reduced precision in the GPS postprocessing can be minimized.
In this paper, we describe GPSQC, a Windowsbased quality control program that gives the variety of information to judge the suitability of GPS observations within the observation time in the field and enhances the precision of data processing. The GPSQC program was designed to directly calculate the geometric distribution, multipath effect, ionospheric delay and cycle slip effect from observation and navigation files at a single point. This program checks the observation data from a single station and is available to run on MS Windows operating systems with GUIs (Window 9x, 2000, XP and higher). The quality of data from any GPS receiver can be checked if the observation data are in the RINEX format. If satellite position information is to be calculated and used by GPSQC, then a RINEX navigation file must also be used. RINEX translators developed at the University of Bern [
GPSQC consists of five data quality/availability parts: geometric distribution (
GPS errors are mainly affected by satellite arrangements [
The forces of gravitational and nongravitational origin perturb the motion of GPS satellites, causing the orbits to deviate from a Keplerian ellipse in inertial space defined by the six elements, semimajor axis a, eccentricity
The
Dilution of precision (
The case of GPS point positioning, which requires the estimation of four parameters (3D position and receiver clock error), the most appropriate
Multipaths are radio signals whereby a GPS signal is reflected off some object reaching the GPS receiver’s antenna. These signals take longer to reach the receiver than if they had travelled along a direct path. As a result, multiple copies of the transmitted signal are present in the tracking loop of the receiver [
Multipaths affect not only the code range and carrierphase measurements but also the measured signal power, which is an average of the composite signal power due to the direct and reflected signal carrier. Although the multipath effect can be reduced by choosing sites without multipath reflectors or by using chokering antennas to mitigate the reflected signal, it is difficult to eliminate all multipath effects from GPS observations. Further, multipath errors are also unique for each receiver and are uncorrelated between signals. Some unmodeled biases remain in GPS observations, even after such data differencing. Therefore, multipath effects are very difficult to calculate. Multipath effects are a major residual error source in the doubledifferenced GPS observables, and they can have a significant impact on the positioning results. If we know the magnitude of multipath effects, then it will be possible to carry the quality control of GPS observations.
The magnitude of multipath effect is defined as the ranging error caused by the reflected carrierphase GPS signal. The multipath effects on the pseudorange observables of L_{1} and L_{2} frequencies (
Multipath analysis creates numerical summaries and graphical displays of pseudorange multipath effects for both single and dual frequency data.
The delay of GPS signals occurs through the ionosphere, which is a shell of electrons and electrically charged atoms and molecules that surrounds the earth, stretching from a height of approximately 50 km to more than 1,000 km. Ionospheric delay is one of the largest sources of error in GPS positioning and navigation and it can vary from a few meters to more than twenty meters within one day [
The magnitude of ionospheric range errors is related to the Total Electron Content (TEC) along the signal path from a GPS satellite to the receiver and is dependent on the signal frequency and the level of ionospheric activity. The TEC is defined as the total number of electrons that are contained in a vertical column with a crosssectional area of 1 m^{2} along the signal path between the satellite and the receiver.
GPS users with dualfrequency Pcode receivers can correct the ionospheric range error through an appropriate combination of the pseudoranges observed on L_{1} and L_{2}. Singlefrequency users with C/Acode receivers do not have that correction. They either have to persevere with the reduced measurement accuracy or employ a model for the correction of ionospheric range errors.
Several electron density models such as Bent [
The Klobuchar model is a simple computational model with an ideal description for the ionosphere’s average behavior. In dual frequency receivers, the ionospheric delay can be estimated precisely by taking advantage of the dispersive nature of the ionosphere. The delay is estimated by measuring the difference in arrival times of the two GPS frequencies.
In this study, we calculated the ionospheric delay (
The ionospheric delay (
Because we are only interested in changes of
Additionally, the ionospheric delay (
The Klobuchar algorithm is based on the shell model or single layer model of the ionosphere. The implicit assumption is that the TEC is concentrated in an infinitesimally thin spherical layer at a certain height, e.g., 350 km [
The ionospheric delay of L_{1} frequency (
The relationship between the time delays on the two frequencies
The temporal rate of change of the ionospheric delay is used to monitor epoch to epoch in order to detect large change in phase ambiguities; that is, any slips in tracking of L_{1} and/or L_{2}. A minimum amount of variability must be assumed because the paths of the signals from the current epoch to the next epoch have changed due to the path change in the ionosphere, the time variation of the ionosphere, the motion of the satellite and the possible motion of antenna itself [
The temporal rate of change as the time derivative of the ionospheric delay (
Cycle slip is an error resulting from the instantaneous loss of carrierphases in GPS observations. Cycle slip is associated with a failure of the carrierphase tracking loop either because the signal is blocked physically or because the signal is weak [
A cycle slip can be detected if double and triple differences are formed using more than two points in GPS carrierphase observations, which are observed simultaneously. In the case of a single GPS observation, however, a different methods for cycle slip detection based on undifferenced carrierphase observations should be applied. In this study, cycle slip detection is performed using the TurboEdit algorithm developed by Blewitt [
The MW linear combination has been widely applied to cycle slip detection in undifferenced observation and double differences. A major advantage of MW combination is that it is not only geometryfree but also ionosphericfree. Therefore, it can be used even if the GPS receiver undergoes high levels of dynamics and/or ionospheric variation. For ionospheric combinations, the MW combination was used in the TurboEdit algorithm to detect and repair cycle slip. The MW combination uses pseudorange observations that have greater noise than carrierphase observations. Under some observation conditions, the pseudorange noise may be much larger than usual, for example, in the presence of multipath, increased ionospheric delay, and low
For observations from a dualfrequency GPS receiver, the MW combination can be expressed as follows,
The widelane ambiguity can be easily obtained from
As long as the phase observations are free of cycle slips, the widelane ambiguity remains quite stable over time. In utilizing the MW combination to detect cycle slips, a recursive averaging filter is used in TurboEdit as follows,
GPSQC was developed using C#.NET language, which is based on MS Windows GUIs. This program gives the user brief information about GPS observations, time series plots and graphs of quality control parameters. It checks the observation and navigation files from a single station and is available to run on a Windows operating system (Windows 9x, 2000, XP and higher) which is widely used in the multitasking environment for PCs. The user interface is relatively easy, as it is based on the Windows series. The fundamental input required by GPSQC is the RINEX Version 2.0 or higher observation and navigation files that contain only GPS data. A RINEX format translator that translates raw data to a RINEX format is publicly available and was developed at the University of Bern.
The program forms linear combinations of the GPS range and carrierphase data to compute the following: (1) the L_{1} pseudorange multipath for C/A or Pcode observations, (2) the L_{2} pseudorange multipath for Pcode observations, (3) the ionospheric phase effects on the L_{1} carrier frequency, and (4) the rate of change of the ionospheric delay. The program also writes data summary files with information about the GPS observations, geometric distribution (
GPSQC will then begin processing. When the processing is complete, the status bar at the bottom of one window will indicate ‘Ready’ and the output will be displayed in another window. The progress bar on the bottom right of the window indicates the amount of time it will take to finish data processing. Graphs and time series of quality control indicators, provided so that the user can easily understand the result of quality control will be displayed in the processing window when the processing is complete.
The report file can be generated automatically by turning on the selective graphing format option for PDF, MS word and Excel formats. Graphs and time series of quality control indicators that may be plotted for a station include the following: satellite azimuth, elevation,
However, we could not define the
To evaluate the suitability of GPSQC, we then analyzed the effect of data quality on the results of precise GPS data processing for daily data. To achieve this, we calculated the quality control indicators using 5 days of GPS observations, from September 1 to 5, 2009 at Suwon station (SUWN), which were registered with the International GNSS Service (IGS). Data processing was carried out by the Precise Point Positioning (PPP) technique from GIPYS/OASISII software, which was developed in the Jet Propulsion Laboratory (JPL) and is capable of calculating to a level of processing equivalent to a few millimeters in GPS data processing [
In this study, a new Windowsbased program, GPSQC with MS GUIs, was developed to improve GPS positioning precision through the efficient quality control of GPS observations. It was applied to actual GPS sensing data, and the following conclusions were found.
Eight quality control parameters (
The GPS data quality results of the IGS station (SUWN) for a fiveday duration that were calculated using the GPSQC program was compared with the positioning results from the precise GPS analysis software (GIPSY/OASISII) to analyze the correlation between the GPS data quality and positioning precision. The results showed that a lower positioning precision led to a lower GPS data quality and that the quality control of GPS sensing data using the proposed GPSQC program is suitable for GPS quality control.
Quality control parameters, their allowable range, and the percentage (%) of content that is related to the quality of GPS observations are provided in timeseries graphs and summary reports in PDF format, and general GPS users can control the GPS data quality more easily and intuitively. This is more convenient than the existing quality control software packages, such as TEQC and GQC because of the userfriendly environment.
Because the quality of GPS sensing data is immediately checked during the GPS observation, the positioning precision from the GPS observations can be checked to determine whether it meets the precision required for the appropriate GPS survey purpose before postprocessing of GPS data. Therefore, the decision can be made quickly in the field regarding whether reobservation or additional observation for precise GPS positioning is required, and time and cost can be saved by eliminating unnecessary GPS observation.
However, further study is required on the correlation between the allowable range of the quality control parameter and its percentage of content for achieving the desired precision to ensure more efficient and economic GPS data quality control. In addition, the Total Electron Content (TEC) factor, which is a main error source of GPS observations [
General flow of the GPSQC program.
Screen shot of interactive start window of GPSQC program.
Screen shot of summary report obtained from GPSQC.
Quality control indicators related to geometric arrangement. (
Quality control indicators related to multipath effects. (
Quality control indicators related to ionospheric effects. (
Quality control indicators related to cycle slip effects (
Descriptions of abbreviations used in Klobuchar model [

 

 

 

 


Criteria and allowance of quality control.
Criteria  > 10  < 5  < 1.0  < 2.0  < ±2.0  < ±10  < ±0.3 
Allowance (%)  90.0  90.0  90.0  90.0  90.0  80.0  80.0 
Comparison of indicators and precision of coordinates.
93.29  100  X : −3062022.8898  
Y : 4055448.0180  
Z : 3841818.2516  
93.34  100  94.46  95.37  90.99  86.80  94.77  X: −3062022.8884  ±0.0048  0.0075  
Y: 4055448.0098  
Z: 3841818.2510  
93.32  100  94.42  95.33  90.90  86.14  94.61  X: −3062022.8875  ±0.0058  0.0086  
Y: 4055448.0092  
Z: 3841818.2514  
100  94.26  95.28  90.46  84.42  94.99  X: −3062022.8880  ±0.0059  0.0088  
Y: 40555448.0063  
Z: 3841818.2486  
100  X: −3062022.8862  
Y: 4055448.0104  
Z: 3841818.2559 