Comparison of some robust parameter estimation techniques for outlier analysis applied to simulated GOCE mission data
Until now, methods of gravity field determination using satellite data have virtually excluded robust estimators despite the potentially disastrous effect of outliers. This paper presents computationally-feasible algorithms for Huber’s M-estimator (a classic robust estimator) as well as for the class of R-estimators which have not traditionally been considered for geodetic applications. It is shown that the computational time required for the proposed algorithms is comparable to the direct method of least squares. Furthermore, a study with simulated GOCE satellite gradiometry data demonstrates that the robust gravity field solution remains almost unaffected by additive outliers. In addition, using robustly-estimated residuals proves to be more efficient at detecting outliers than using residuals resulting from least squares estimation. Finally, the non-parametric R-estimators make less assumptions about the measurement errors and produce similar results to Huber’s M-estimator, making that class a viable robust alternative.
KeywordsGOCE satellite gradiometry robust parameter estimation rank norm outlier diagnostics
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