Comparing different global positioning system data processing techniques for modeling residual systematic errors

In the case of traditional Global Positioning System (GPS) data processing algorithms, systematic errors in GPS measurements cannot be eliminated completely, nor accounted for satisfactorily. These systematic errors can have a significant effect on both the ambiguity resolution process and the GPS positioning results. Hence this is a potentially critical problem for high precision GPS positioning applications. It is therefore necessary to develop an appropriate data processing algorithm which can effectively deal with systematic errors in a nondeterministic manner. Recently several approaches have been suggested to mitigate the impact of systematic errors on GPS positioning results: the semiparametric model, the use of wavelets, and new stochastic modeling techniques. These approaches use different bases and have different implications for data processing. This paper aims to compare the above three methods, in both the theoretical and numerical sense.