发表机构
TÜBİTAK UZAY; Istanbul Technical University（土耳其科学技术研究理事会空间技术研究所; 伊斯坦布尔理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对LEO卫星合点风险，提出基于无迹变换敏感性分析和PCA特征选择，构建增强CDM数据集，并用混合TCN-Transformer模型预测后续CDM的碰撞概率，实现早期风险评估。
AI 中文摘要
运行卫星和轨道碎片的快速增加提高了低地球轨道（LEO）中近距离接近事件的频率，给卫星运营商带来了更高的操作负担。这个问题对于使用电推进的卫星尤为关键，因为低推力机动能力对碰撞规避规划施加了额外的时间限制。在当前实践中，合点数据消息（CDMs）提供相对状态、协方差、最小接近距离、最接近时间以及碰撞概率（PoC）信息用于合点评估。然而，轨道不确定性的非线性传播以及PoC对协方差演化的敏感性使得对连续CDMs的解释具有挑战性。本研究提出了一种基于学习的框架，通过估计同一近距离接近事件在后续CDM更新中预期的PoC来早期预测卫星合点风险。在所提出的方法中，首先使用基于无迹变换的传播和反向传播框架来评估碰撞风险度量对CDM参数的敏感性。此外，对数值CDM参数应用主成分分析（PCA）以识别与PoC变化最相关的特征。然后利用敏感性分析和PCA获得的结果来论证所选原始CDM参数的合理性，并构建表示相对运动、交会几何和协方差相关不确定性的派生度量。利用由此产生的序列化增强合点数据集，训练了一个混合时间卷积网络（TCN）-Transformer模型来学习合点风险的时间演化。该框架应用于TÜBİTAK UZAY内接收和分析的CDMs，展示了其在LEO卫星合点中实现更早且更一致的操作风险评估的潜力。
英文摘要
The rapid expansion of operational satellites and orbital debris has increased the frequency of close approach events in low Earth orbit (LEO), creating a higher operational burden for satellite operators. This problem is especially critical for satellites using electric propulsion, where low-thrust maneuver capability imposes additional time constraints on collision avoidance planning. In current practice, Conjunction Data Messages (CDMs) provide relative state, covariance, miss distance, time of closest approach, and probability of collision (PoC) information for conjunction assessment. However, the nonlinear propagation of orbital uncertainties and the sensitivity of PoC to covariance evolution make the interpretation of sequential CDMs challenging. This study proposes a learning-based framework for early prediction of satellite conjunction risk by estimating the PoC expected in the subsequent CDM update of the same close approach event. In the proposed methodology, an Unscented Transform-based propagation and backpropagation framework is first used to evaluate the sensitivity of the collision risk metric to CDM parameters. In addition, Principal Component Analysis is applied to the numerical CDM parameters to identify the features most relevant to PoC variation. The results obtained from the sensitivity analysis and PCA are then used to justify the selected raw CDM parameters and to construct derived metrics representing relative motion, encounter geometry, and covariance-related uncertainty. Using the resulting sequential enriched conjunction dataset, a hybrid Temporal Convolutional Network (TCN)-Transformer model is trained to learn the temporal evolution of conjunction risk. The framework is applied to CDMs received and analyzed within TÜBİTAK UZAY, demonstrating its potential for earlier and more consistent operational risk evaluation for LEO satellite conjunctions.
CommentsAccepted in 2026 AAS/AIAA Astrodynamics Specialist Conference
