Abstract
GPS (Global Positioning System) devices can be used in many applications which require accurate point positioning in geosciences. Accuracy of GPS decreases due to outliers resulted from the errors inherent in GPS observations. Several approaches have been developed to detect outliers in geodetic observations. It is important to determine which method is most effective at distinguishing outliers from normal observations. This paper investigates the behavior of conventional statistical test methods (Data Snooping (DS), Tau and t tests), some robust methods (Andrews's M-Estimation, Huber's M-Estimation, Tukey's M-Estimation, Danish Method, Yang-I M-Estimation, Yang-II M-Estimation, and fuzzy logic method in detection of outliers for three GPS networks having different characteristics. Test results are evaluated and the performances of different methods are presented quantitatively.
Highlights
Geoscience applications such as determination of crustal movements, deformations and landslides require accurate point positioning
If the conventional methods are used at very small significance levels, these methods tend to mask the outliers
In the first and second GPS networks, there appeared no outliers at any significance level
Summary
Geoscience applications such as determination of crustal movements, deformations and landslides require accurate point positioning. GPS receivers measure code and phase to every satellite. In relative positioning (at millimeter level), at least two GPS receivers are occupied at two control points (position of one control point is known) and the code and phase observations to at least four GPS satellites are measured simultaneously. These measurements are repeated for a certain period of time which leads to redundant observations. The baseline components of these two points are measured using GPS receivers and the X, Y, Z coordinates of point B are obtained as:
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