用生成模型从角度观测推断月球轨道初始状态，兼顾物理规律与不确定性。
Physics-informed Conditional Normalizing Flows for Angles-only Cislunar Orbit Determination
- 基于条件归一化流建模角度观测下的轨道初始状态分布
- 在短弧观测下生成符合物理规律的多模态状态假设，精度优于传统方法
- 适合需要快速、可靠初始估计的深空探测任务
本文将生成式动力学拓展至地月空间轨道确定问题。任务被形式化为条件密度估计，目标是从短观测弧段的角度观测中推断初始状态的概率分布。利用来自近直线晕轨道（Near Rectilinear Halo Orbits）的受扰地心观测数据训练归一化流模型，实现对后验分布的灵活且可能多模态的表征。给定新观测后，通过采样学习到的密度生成统计一致且物理自洽的状态假设，并通过非线性最小二乘法进一步优化，为经典算法提供具有竞争力的初始值。
原文摘要 · Abstract (English)
Generative Astrodynamics is advanced in this work by extending generative modelling to an orbit determination problem in the cislunar environment. The task is formulated as conditional density estimation, aiming to infer the probability distribution of the initial state from angles-only measurements over short observation arcs. A normalising flow is trained on perturbed topocentric observations from Near Rectilinear Halo Orbits, enabling a flexible and potentially multimodal posterior representation. Given new measurements, the learned density is sampled to generate statistically consistent and physics-informed state hypotheses. These estimates are refined via nonlinear least-squares minimisation, providing a competitive warm start for classical algorithms.
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