发表机构
Johns Hopkins University（约翰斯·霍普金斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出约束扩散模型增强稀缺轨道数据，在星座任务中实现局部支持内增强，但无法替代轨道动力学或作为操作后验，轨道总体构建是星座分析的重要不确定性来源。
AI 中文摘要
星座蒙特卡洛结果取决于用于评估任务策略的轨道总体。在参考轨迹稀缺的情况下，重放限制了几何多样性，而独立的轨道元素抖动可能违反物理约束。我们研究了用于轨道总体增强的约束扩散。一个力条件扩散模型学习13维轨道先验，从近地点高度和偏心率恢复半长轴；Basilisk在五种力模型层级之一下传播每个样本。使用800条参考轨迹，我们将扩散与抖动自助法、每层高斯混合模型以及条件变分自编码器进行比较，并评估分布保真度、支持偏移、经典天体动力学诊断以及4,000个配对的GoDSAT兼容战役。最大的扩散模型实现了留出轨迹MMD为0.0171±0.0239，与自助法（0.0170）和混合模型（0.0198）相似，但样本距离训练先验更远（中位最近训练距离为2.7对0.10标准化单位）。所有生成器均未能匹配偏移盲总体（分类器AUC为0.991-0.998）。端点条件样本满足Lambert边界，但内部误差大于匹配的Lambert参考（平均RMSE为29.2对5.7公里）。残差扩散改善了选定的稀疏预测和目录模式召回，但在监护排名上未优于经典估计器。在固定的16卫星配置中，扩散产生的平均监护与重放和自助法相似，而力层级混合将监护偏移约5.5个百分点。约束扩散支持局部、支持内增强，但不能替代轨道动力学或作为操作后验。轨道总体构建是星座分析中一个重要的不确定性来源。
英文摘要
Constellation Monte Carlo results depend on the orbital population used to evaluate a tasking policy. With scarce reference trajectories, replay limits geometric diversity, while independent orbital-element jitter can violate physical constraints. We study constrained diffusion for orbital-population augmentation. A force-conditioned diffusion model learns a 13-dimensional orbital prior, recovering semimajor axis from perigee altitude and eccentricity; Basilisk propagates each sample under one of five force-model tiers. Using 800 reference trajectories, we compare diffusion with jittered bootstrap, per-tier Gaussian mixtures, and a conditional variational autoencoder, and evaluate distributional fidelity, support shift, classical astrodynamics diagnostics, and 4,000 paired GoDSAT-compatible campaigns. The largest diffusion model achieves held-out trajectory MMD of 0.0171 +/- 0.0239, similar to bootstrap (0.0170) and the mixture (0.0198), but samples farther from training priors (median nearest-training distance 2.7 versus 0.10 standardized units). All generators fail to match the shifted-blind population (classifier AUC 0.991-0.998). Endpoint-conditioned samples satisfy Lambert boundaries but have greater interior error than the matched Lambert reference (29.2 versus 5.7 km mean RMSE). Residual diffusion improves selected sparse forecasts and catalog-mode recall but does not outperform classical estimators on custody ranking. In a fixed 16-satellite configuration, diffusion yields similar mean custody to replay and bootstrap, while the force-tier mixture shifts custody by about 5.5 percentage points. Constrained diffusion supports local, in-support augmentation but cannot replace orbital dynamics or serve as an operational posterior. Orbital-population construction is a consequential source of uncertainty in constellation analysis.
Comments16 pages. Submitted to the 2027 IEEE Aerospace Conference; under review
