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.

翻译：本文提出一种用于观测数据预处理以提高轨道确定工具鲁棒性的算法。该算法实现两个目标：获取初始轨道确定问题的改进解，并检测处理测量值中可能存在的异常值。通过利用给定传播窗口内的传感器数据，对初始估计的不确定性进行正向传播并逐步降低。采用微分代数技术与一种用于二阶泰勒展开的新型自动域分割算法，以高效地对随时间变化的不确定性进行传播。运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至可观测空间获得多项式映射。剔除与实际测量值不重合的域，从而降低需进一步传播的不确定性，并在此步骤中检测测量异常值。随后，利用改进后的估计值与保留的观测值提升批处理轨道确定工具的鲁棒性。基于TAROT网络提供的合成与实际观测数据，针对地球静止转移轨道目标的算法有效性得以验证。