标题： 一种应用于外行星轨道器估计的GBEES贝叶斯校准 显示英文标题

标题： A Bayesian Benchmarking of GBEES Applied to Outer Planet Orbiter Estimation

摘要： 基于矩的估计滤波器在几十年来成功地帮助了航天器导航。 然而，未来的任务计划进入深空区域，存在显著的往返光时通信延迟，在不稳定、准周期轨道中运行，并对外行星卫星进行高精度、低高度飞越。 这些复杂的轨迹可能需要基于集合的滤波器，在实际测量频率下进行准确估计。 为了减轻测试新型导航软件所带来的固有风险，集合滤波器必须准确、高效且稳健。 基于网格的、利用稀疏性的贝叶斯估计，一种高维Godunov型有限体积方法，能够传播完整的概率分布函数，在与当前滤波器领域的比较中，展示了在所有这些标准下的强大整体性能。 这些特性通过一个贝叶斯研究得到体现，在该研究中，土星-恩克拉多斯远距离逆行轨道的状态不确定性被传播，并结合了不频繁的非线性测量更新。 除了均方根误差外，我们还使用Bhattacharyya系数，这是一种用于测量分布之间差异性的非正态指标，以及有效采样大小，这是一种衡量粒子退化程度的指标，以定量确认在此应用中，基于网格的、利用稀疏性的贝叶斯估计优于其他评估的集合滤波器，尽管它带来了非微小的计算成本。 显示英文摘要

摘要： Moment-based estimation filters have successfully aided spacecraft navigation for decades. However, future missions plan to venture into deep-space regimes with significant round-trip light-time telecommunication delays, operate in unstable, quasi-periodic orbits, and perform highly precise, low-altitude flybys of outer planet moons. These complex trajectories may necessitate ensemble-based filters for accurate estimation over realistic measurement cadences. To mitigate the inherent risk associated with testing novel navigation software, ensemble filters must be accurate, efficient, and robust. Grid-based, Bayesian Estimation Exploiting Sparsity, a high-dimensional Godunov-type finite volume method that propagates the full probability distribution function, demonstrates strong overall performance across all these criteria when compared with the contemporary landscape of filters. These qualities are exhibited via a Bayesian investigation in which the state uncertainty of a Saturn-Enceladus Distant Prograde Orbit is propagated, incorporating infrequent, nonlinear measurement updates. Along with root mean square error, we use the Bhattacharyya coefficient, a non-normal metric for measuring the dissimilarity between distributions, and the Effective Sampling Size, a measure of particle degeneracy, to quantitatively ascertain that in this application, Grid-based, Bayesian Estimation Exploiting Sparsity outperforms the other ensemble filters assessed, though it comes at a nontrivial computational cost.