期刊
ACTA ASTRONAUTICA
卷 214, 期 -, 页码 277-292出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.actaastro.2023.10.040
关键词
Uncertainty propagation; Multifidelity methods; Robust orbit determination
This paper presents an algorithm for improving the robustness of orbit determination tools through the preprocessing of observation data. The algorithm progressively reduces the uncertainty in the initial estimate by utilizing sensor data and detects outliers in the processed measurements. It uses differential algebra techniques and a novel automatic domain splitting algorithm to efficiently propagate uncertainties over time and employs a multifidelity approach to minimize computational effort while maintaining accuracy. The algorithm's effectiveness is demonstrated using synthetic and real observation data for a geostationary transfer orbit object.
This paper presents an algorithm for the preprocessing of observation data aimed at improving the robustness of orbit determination tools. Two objectives are fulfilled: obtain a refined solution to the initial orbit determination problem and detect possible outliers in the processed measurements. The uncertainty on the initial estimate is propagated forward in time and progressively reduced by exploiting sensor data available in said propagation window. Differential algebra techniques and a novel automatic domain splitting algorithm for second-order Taylor expansions are used to efficiently propagate uncertainties over time. A multifidelity approach is employed to minimize the computational effort while retaining the accuracy of the propagated estimate. At each observation epoch, a polynomial map is obtained by projecting the propagated states onto the observable space. Domains that do no overlap with the actual measurement are pruned thus reducing the uncertainty to be further propagated. Measurement outliers are also detected in this step. The refined estimate and retained observations are then used to improve the robustness of batch orbit determination tools. The effectiveness of the algorithm is demonstrated for a geostationary transfer orbit object using synthetic and real observation data from the TAROT network.
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