Skip to content

Highlights of my research work in MATLAB, statistical modeling of the unstructured raw data from GPS satellites for several years. Data modeling and processing, followed by different residual plots including trends and root mean square. In the end, the result was compared with independent data set models for validation purposes. The results were…

Notifications You must be signed in to change notification settings

ttariqaziz/statistical_modeling_matlab

Repository files navigation

statistical_modeling_matlab

Highlights of my research work include MATLAB-based statistical modeling of unstructured raw data from GPS satellites over several years. The research involved data modeling and processing, followed by generating various residual plots, including trends and root mean square analysis. Finally, the obtained results were compared with independent data set models to validate the findings. The research outcomes were also presented at a European conference.

Map of Geodetic Stations in Sweden

img

Station Types

First-order Station (left) Second-order Station (right), (Images courtesy of SWEPOS, Sweden) img

Good and Bad Geometry

img

Error Sources

img

Space Weather Sources

img

Residual Plots

img

Zenith Hydrostatic and Wet Delay Plots

img

Metrics

Station ID RMS (mm) Mean Difference (m) Standard Deviation (m)
ALB0 5.829 ECMWF Model: 0.0041 ECMWF Model: 0.0062
RCA Model: 0.0046 RCA Model: 0.0071
GYRO 5.428 ECMWF Model: 0.0016 ECMWF Model: 0.0066
RCA Model: 0.0016 RCA Model: 0.0067
HISO 5.676 ECMWF Model: 0.0031 ECMWF Model: 0.0062
RCA Model: 0.0030 RCA Model: 0.0063
NYBO 5.673 ECMWF Model: 0.0042 ECMWF Model: 0.0068
RCA Model: 0.0042 RCA Model: 0.0068
OVTO 5.275 ECMWF Model: 0.0032 ECMWF Model: 0.0071
RCA Model: 0.0032 RCA Model: 0.0070
OXEO 6.938 ECMWF Model: 0.0013 ECMWF Model: 0.0074
RCA Model: 0.0012 RCA Model: 0.0075
STAO 7.095 ECMWF Model: 0.0013 ECMWF Model: 0.0067
RCA Model: 0.0012 RCA Model: 0.0069
VASO 7.095 ECMWF Model: 0.0018 ECMWF Model: 0.0080
RCA Model: 0.0018 RCA Model: 0.0080

Conclusion

  • Until the year 2010, only 25 first stations were used for measuring Tropospheric delay measurement. However, after these results and validation, we can conclude that these nine second-order stations could also be usable for measuring Tropospheric delay estimation.
  • One of the biggest advantages of using this technique is its cost-effectiveness compared to the much more expensive balloon and airborne techniques, as it relies on the existing infrastructure.
  • Secondly, GNSS measurements can be obtained at any time and in any weather condition, making it highly accessible.
  • For more sophisticated measurements, we can use balloons and airborne systems, but launching those systems requires proper planning and is limited to a few times per year, whereas GNSS systems work 24/7 and function in all weather conditions.
  • From all the results shown in this thesis, we can confidently state that these second-order stations are usable for monitoring Tropospheric water vapor activity and delay estimation.

About

Highlights of my research work in MATLAB, statistical modeling of the unstructured raw data from GPS satellites for several years. Data modeling and processing, followed by different residual plots including trends and root mean square. In the end, the result was compared with independent data set models for validation purposes. The results were…

Topics

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published