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 pruned 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.

翻译：本文提出了一种观测数据预处理算法，旨在提升轨道确定工具的鲁棒性。该算法实现两个目标：获得初始轨道确定问题的改进解，并检测处理测量值中的潜在异常值。通过利用传播时间窗口内的传感器数据，初始估计的不确定性被向前传播并逐步降低。采用微分代数技术和一种针对二阶泰勒展开的新型自动域分割算法，以高效地随时间传播不确定性。采用多保真度方法在保持传播估计精度的同时最小化计算量。在每个观测历元，通过将传播状态投影到可观测空间获得多项式映射。与实测值无重叠的域被剪枝，从而减少后续传播的不确定性，并在此步骤中检测测量异常值。随后，利用改进的估计和剪枝后的观测值来提升批处理轨道确定工具的鲁棒性。利用TAROT网络的合成及真实观测数据，针对地球静止转移轨道目标验证了该算法的有效性。