An investigation into the correlations among GNSS observations and their impact on height and zenith wet delay estimation for medium and long baselines
Taylor & Francis
School of Engineering
Most stochastic modelling techniques neglect the correlations among the raw un-differenced observations when forming the variance–covariance matrix of the Global Navigation Satellite System (GNSS) observations. Some methods were developed to model these correlations. One such method is the Minimum Norm Quadratic Unbiased Estimator (MINQUE). Studies have shown that MINQUE improves ambiguity resolution, and ultimately, the positioning solution in short baselines. However, its effect in cases of processing with longer baselines and on the estimation of zenith wet delay (ZWD) is somewhat unknown. In this paper, a comparison between the impact of neglecting the correlations among the observations using an elevation-angle-dependent model (EADM) and modelling the correlations using MINQUE on height determination and ZWD for medium and long baselines is carried out. The initial testing was carried out across two Australian GNSS stations with a medium-length baseline throughout a three-week campaign. The results showed that using MINQUE did not resolve the coordinate, height and wet delay components as accurately as the EADM. The results were further verified with two long-baseline campaigns whereby EADM was also able to provide better wet delay estimates. The coordinate results were, however, mixed. Overall, the study concluded that the inclusion of the correlations among the observations, in general, do not improve the resolution of the coordinate and wet delay estimates.