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Currently, GPS campaign measurements (i.e. repeated GPS measurements) are used frequently in order to determine geophysical phenomena such as tectonic motion, fault zones, landslides, and volcanoes. The coordinates of a new point installed in a study area are usually found either by using relative point positioning or precise point positioning (PPP). Employing observation sessions shorter than 24h might still be a necessity at times. When observation duration is shorter, the accuracy of coordinates are degraded and also the accuracy of point velocities are affected. The accuracies of the geodetic site velocities from a global network of the International GNSS Service (IGS) stations were previously investigated using only PPP. In this study, we extend that study in which site velocities will also be assessed including fundamental relative positioning. PPP derived results will also be evaluated to see the effect of JPL reprocessed products and single receiver ambiguity resolution. IGS is a good data source for simulation studies and hence globally distributed 18 continuously operating IGS stations were chosen to create synthetic GPS campaigns. GPS data were processed comparatively using GAMIT/GLOBK v10.6 and GIPSY/OASIS II v6.3. The data of synthetic campaign GPS time series were processed using a regression model accounting for the linear and seasonal variation of the ground motion. Once accepting the velocities derived from 24h sessions as the truth, the results from sub-sessions were compared with the results of 24h and hypothesis testing was applied for the significance of the differences. The major outcome of this study is that at global scales (i.e. over long distances) with short observation sessions, the fundamental relative positioning produces similar results to PPP. The reliability of the velocity estimation for horizontal components has now been improved to about 85% on the average for observation durations of 12h. [ABSTRACT FROM AUTHOR]

In this study, the effect of the large height difference on commercially available GNSS baseline solutions is tested. In addition, comparisons to academic software results are provided. Previously the effect of the large height difference was tested on GIPSY relative-PPP results. A sub-network of the IGS consisting of 26 baselines was chosen and the height difference between baseline points was gradually incremented up to about 1650 m. Varying observation sessions of 1-24 hours were generated and processed using the continuous data of the IGS stations. The baseline length between ground stations was around 10 km. Atmosphere related errors over this scale cancel out with relative positioning when there is no height difference between baseline points. Within the frame of this study, we almost adopted the similar experiment design. Differently, we processed the GNSS data using the commercially available Topcon Magnet, v. 4.0.1 software. The results reveal that in terms of horizontal positioning, both the research and commercial software produce more or less similar results. In terms of vertical positioning, as expected, the results of commercially available software are far poorer than those of the research software. Obviously not estimating the troposphere, i.e. using only a standard troposphere model, adversely affected the results of vertical positioning. The biggest impact was on the finding that the accuracy of vertical positioning ranged between 20-90 millimetre over similar scales even if the observation session was extended up to 24 h. This would probably break the routine of monitoring high structures over local scales with only a couple of hours of observation sessions and commercial software with a high expectation of precision results. [ABSTRACT FROM AUTHOR]

There are many error sources affecting the GPS precision. In this study, we focus on howGPS measurements (i.e. baseline components n, e, up that form GPS time series) are affectedby the disturbances in the ionosphere. The solar activity is one such error source. Toquantify the effect of solar activity, sunspot numbers and the solar cycle are taken intoaccount. For this aim, we generated GPS campaign measurements one day per each monthbetween 2002 and 2018. Thus the period of solar cycle extending about 11 years was takeninto account. We designed an experiment in which 6 GPS stations from different latituderegions of the International GNSS Service were used, and the data was sampled to be 8-14hours GPS campaigns. PPP module of the NASA/JPL’s GIPSY/OASIS II software was usedfor the processing. We examined the correlation between the number of sunspots and thestandard error of the coordinate components constituting the campaign GPS time series. Theeffect of the solar activity was investigated for 3 different periods (solar minimum,solar moderate and solar maximum) with respect to the intensity of the number ofsunspots. Along the solar cycle, positive and negative correlations between sun spotnumbers and standard errors were found at some of the stations. This deterioratesthe positioning accuracy level which was previously found using the traditionalways. Keywords: Solar Activity, Accuracy, GNSS, PPP [ABSTRACT FROM AUTHOR]

GPS techniques have been widely used to determine geophysical phenomena which isestimated from time series using linear regression. Mis- or non-modeled error sources (i.e.atmospheric propagation etc.) and monument types lead to noise in the time series. The noisein the series is described as white plus flicker noise combinations. Estimation of unbiasedtrend and its realistic uncertainties are the primary duty for a reliable conclusions. Should thenoise in the series from GPS campaign measurements be introduced, while it is possible toanalyze for a continuous stations’ data? To find an answer to the question, we simulated time series using Hector v1.6. The lengthof the series is 10 years, its standard deviations of driving noise are between the range of 1.3and 4.0 mm with the increments of 0.1 mm; moreover, white and flicker noise fractions havebeen selected as 0.50. Firstly, the noise amplitudes accepted as the truth were calculated fromsimulated series including no gaps via variance component estimation. 3, 7, and 10 dayscampaign set-ups were created. For instance, first 3 consecutive days (January 1st through3rd) from each year in the series were selected and analyzed. Then, the second group(January 4th through the 6th) were analyzed by shifting the previous 3 consecutive days, andso on. This set-up were applied for 7, and 10 days campaigns. After that noiseamplitudes were estimated from 121, 52, and 36 time series from set-up campaigns for 3,7, and 10 days campaigns, respectively. It is hard to estimate noise amplitude forthe series from campaigns due to the small number of measurements. When wecompare the results of GPS campaigns with those of the truth, we conclude thatthe correlations between campaigns need to be taken into account even thoughthere is a bias between the true values and the amplitudes of GPS campaign timeseries. Keywords: Time series, noise analysis, GPS campaign [ABSTRACT FROM AUTHOR]

Installing GNSS permanent stations requires high costs. Besides, the installation is not possible in all types of deformation monitoring. On the other hand, the continuous data is the standard for reliable accuracy and velocity estimation. How can one achieve comparable accuracies with less cost and similar accuracy in studies such as deformation monitoring? One way to reduce the cost is to perform data collection in episodic campaign measurements. In fact GNSS campaigns are a necessity for studies such as landslide monitoring in which the deformation would harm permanent installations. The repetition interval (i.e. the frequency) and observation duration of campaign observations need to be studied for reliable velocity estimation. Previously, GNSS continuous time series were decimated into monthly sampled campaigns (i.e. one campaign per month), and velocity estimation was performed from those campaigns. The data archives of the International GNSS service (IGS) and the time series of the Jet Propulsion Laboratory (JPL) were used for the purpose. Although the spectral character (i.e. amplitude and phase of common periodicities) slightly changed, the velocity estimated from those campaigns produced comparable accuracies to those of the GNSS continuous data. In this study, we reduced the data of the time series forming the GNSS campaigns 3 campaigns per year. This would be a trade-off between the traditional 1 campaign per year procedure in which horizontal velocities are estimated only with about 80% confidence level and the recent procedure 12 campaigns per year producing comparable velocity estimation to the continuous GNSS. Decimating GNSS campaigns as sparse as 4 months more or less eliminates the time correlation between the successive measurements. On the other hand the procedure produces almost equivalent results to the continuous data. [ABSTRACT FROM AUTHOR]