Gross Error Compensation for Gravity Field Analysis Based on Kinematic Orbit Data

This paper aims at a comparative study of several measures to compensate for gross errors in kinematic orbit data. It starts with a simulation study on the influence of a single outlier in the orbit data on the gravity field solution. It is shown that even a single outlier can degrade the resulting gravity field solution considerably. To compensate for outliers, two different strategies are investigated: wavelet filters, which detect and eliminate gross errors, and robust estimators, which due to an iterative downweighting gradually ignore those observations that lead to large residuals. Both methods are applied in the scope of the analysis of a 2-year kinematic CHAMP (challenging minisatellite payload) orbit data set. In various real data studies, robust estimators outperform wavelet filters in terms of resolution of the derived gravity field solution. This superior performance is at the cost of computational load, as robust estimators are implemented iteratively and require the solution of large sets of linear equations several times.This paper aims at a comparative study of several measures to compensate for gross errors in kinematic orbit data. It starts with a simulation study on the influence of a single outlier in the orbit data on the gravity field solution. It is shown that even a single outlier can degrade the resulting gravity field solution considerably. To compensate for outliers, two different strategies are investigated: wavelet filters, which detect and eliminate gross errors, and robust estimators, which due to an iterative downweighting gradually ignore those observations that lead to large residuals. Both methods are applied in the scope of the analysis of a 2-year kinematic CHAMP (challenging minisatellite payload) orbit data set. In various real data studies, robust estimators outperform wavelet filters in terms of resolution of the derived gravity field solution. This superior performance is at the cost of computational load, as robust estimators are implemented iteratively and require the solution of large sets of linear equations several times.