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。该算法实现了两个目标：获得初始轨道确定问题的精化解，并检测处理测量数据中可能存在的异常值。通过利用传播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。采用微分代数技术与一种新颖的基于二阶泰勒展开的自动区域分割算法，实现不确定性随时间的高效传播。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开销。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。与实测数据无重叠的区域被裁剪，从而减少后续传播的不确定性。在此步骤中还可检测测量异常值。精化后的估计与保留的观测数据随后用于提升批量轨道确定工具的鲁棒性。利用TAROT网络合成与真实观测数据，以地球静止转移轨道目标为例验证了该算法的有效性。