As mentioned before, four main data sets were processed in this thesis. These data sets were selected to cover the investigation factors that affect the kinematic PPP estimation. The following is a brief description of the processing procedure for each data set and the answer to questions about the use of each data set. Threemain parameters have been investigated in this study, as follows:
1. The impact of the interval of the satellite clocks on the kinematic PPP estimation. To establish the relationship between the accuracy of the kinematic PPP measurements and the interval of satellite clocks, two satellite clock products have been examined: CODE with an interval of 30 s, and CODE with an interval of 5 s. The processed data sets have a sampling interval of 5 s and 1 s. The reason for using a data sampling interval of 5 s is that the data interval matches the satellite clock of CODE with the interval of 5 s; this causes the ideal processing case. The major objective of using a data sampling interval of 1 s was to clarify the effect of the satellite clocks interval on such high rate measurement data. The flowchart of the processing concept regarding the satellite clock interval is given in Figure 5.15. For this investigation, different data sets have been identified as follows:
a) One static data set: The first data sets of four CORS stations that have been downloaded from SAPOS were processed twice; once, with an observation sampling interval of 5 s; second, with an observation sampling interval of 1 s.
b) Two kinematic data sets (Hydro measurements): the first kinematic data set is the third data set that has been observed on the Rhine River, Duisburg, Germany with an observation interval of 5 s. The second data set was the fourth data set that was surveyed on the River Nile with an observation sampling interval of 1 s.
68 Concept and Realization of Measurements
Figure 5.15: Flowchart of the satellite clock investigation process
2. The impact of the troposphere model on the kinematic PPP estimation. Three troposphere models were investigated:
NMF, which is based on the Saastamoinen model plus Niell as model mapping function;
a GMF model with a dry troposphere estimation based on the atmosphere data from
the GPT model, which is estimated using the Saastamoinen model.
The other model is based on the grids of a VMF1 model which is based on the atmosphere data provided by the ECMWF.
The objective of this study investigation is to evaluate the qualitative and quantitative influence of the three troposphere models on the kinematic estimation. In addition, it aims to investigate the effect of the three models on the kinematic PPP estimation. A static data set and all hydrographic kinematic data sets have been examined. The flowchart of the processing concept regarding the troposphere modelling is shown in Figure 5.16. These data sets are as follows:
a) One static data set: the static data set is the second data set of 45 IGS’s stations. The RINEX files have an observation time of 24 hours with a sampling interval of 30 s. One advantage of the analysis of these stations is that these stations cover different climates around the world, which reflect various atmosphere parameters. This advantage offers some important insights into the variation of the climate regions and their effect on the kinematic PPP estimation. Another advantage from this analysis is that the 24-hour observation time provides a longer observation time than the one of the real kinematic experimental works.
b) All kinematic data sets (Hydro measurements): the kinematic data sets are the third and fourth data sets. Figure 5.16 shows the processing procedure for this parameter.
Figure 5.16: Flowchart of the analysis strategy for troposphere modelling
C O D E/30 s C O D E/5 s Satellite clock Interval Observation Sampling 5 s Observation Sampling 1 s
First data set
Third data set
First data set
Fourth data set
Fourth data set Troposphere delay model G MF/ G PT V MF1/ EC M WF
Second data set
Third data set
N
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3. The impact of height constraining on kinematic PPP positioning.
This study section provides an exciting opportunity to advance our understanding of the way to improve the estimated hydrographic kinematic PPP position by constraining the height. The concept of height constraining has been investigated for three hydrographic trajectories. Two trajectories of the third data set were observed on the Rhine River, Germany. One trajectory of the fourth data set was observed on the River Nile, Egypt. These trajectories provided a high positional error after the previous investigations of satellite clocks and troposphere. Figure 5.17 concludes the analysis scenario of the height constraining concept. Two main aspects are applied for this concept as follow:
a) Concept of stability of the water level: this concept is based on considering one height to be constrained for the whole trajectory. One trajectory is examined for this concept. b) Concept of piecewise stability of the water level: this concept aims to consider the
piecewise stability of the height over the time in series sessions. For each session, a mean height and a standard deviation have to be estimated and be used for height constraining. The procedure is based on the detection of the different sessions automatically using a code that is programmed in MATLAB. This code defines the sessions according to the user conditions for the length of these sessions and the required standard deviation for the height. Two trajectories are applied for this concept of automatic detection of piecewise stability of the water level.
Figure 5.17: Flowchart of analysis strategy for height constraining