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In robust estimation theory, two important concepts characterize the robustness of an estimator: the breakdown point and the influence function.
To account for the impact of the morphological distortion as well as outliers in the survey data, the correlation matching is adjusted based on the robust estimation theory.
New mathematical fundamentals for filtering have appeared steadily during the last 40 years as robust estimation theory in 70 and 80th [10, 13], wavelets, PCA and ICA in 90th of the previous century [11, 14] have been developed.
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Firstly, according to robust estimation theories, the characteristic weight function is designed for residuals and measurement outputs.
Based on star-coordinate matching, a robust weight-tuning rule is designed according to the maximum-likelihood estimation theory, including a method to determine the star-coordinate confidence scope by statistically analysing the astronomical-observation perturbation rate.
Robust estimation without source knowledge, and 5.
Thus, a more robust estimation may significantly improve the performance.
See Section 2.4 for possible robust estimation methods.
The developed theoretical framework includes robust estimation and filtering methods, as well as robust error metrics.
Therefore, the robust estimation of W pf (k) in (19) asks for the robust estimation of g(ϕ i,k).
Special techniques are suggested for robust estimation of priority vectors.
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Justyna Jupowicz-Kozak
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