Current approaches for collision avoidance and space traffic management face many challenges, mainly due to the continuous increase in the number of objects in orbit and the lack of scalable and automated solutions. To avoid catastrophic incidents, satellite owners/operators must be aware of their assets' collision risk to decide whether a collision avoidance manoeuvre needs to be performed. This process is typically executed through the use of warnings issued in the form of CDMs which contain information about the event, such as the expected TCA and the probability of collision. Our previous work presented a statistical learning model that allowed us to answer two important questions: (1) Will any new conjunctions be issued in the next specified time interval? (2) When and with what uncertainty will the next CDM arrive? However, the model was based on an empirical Bayes homogeneous Poisson process, which assumes that the arrival rates of CDMs are constant over time. In fact, the rate at which the CDMs are issued depends on the behaviour of the objects as well as on the screening process performed by third parties. Thus, in this work, we extend the previous study and propose a Bayesian non-homogeneous Poisson process implemented with high precision using a Probabilistic Programming Language to fully describe the underlying phenomena. We compare the proposed solution with a baseline model to demonstrate the added value of our approach. The results show that this problem can be successfully modelled by our Bayesian non-homogeneous Poisson Process with greater accuracy, contributing to the development of automated collision avoidance systems and helping operators react timely but sparingly with satellite manoeuvres.

翻译：当前碰撞避免与空间交通管理方法面临诸多挑战，主要源于轨道物体数量持续增长以及缺乏可扩展的自动化解决方案。为避免灾难性事件，卫星所有者/运营者必须掌握其资产的碰撞风险，从而决定是否执行碰撞规避机动。该过程通常通过分析以CDM形式发布的警告信息实施，其中包含预期TCA、碰撞概率等事件参数。我们此前的工作提出了一种统计学习模型，能够回答两个关键问题：(1) 在指定时间间隔内是否会发布新的会合预警？(2) 下一份CDM到达的时间及不确定性如何？然而，该模型基于经验贝叶斯齐次泊松过程，假设CDM到达率随时间恒定不变。实际中，CDM发布频率既受物体运动行为影响，也取决于第三方执行的筛查过程。为此，本研究在先前工作基础上进行拓展，提出一种采用概率编程语言高精度实现的贝叶斯非齐次泊松过程，以完整描述潜在现象。我们将所提方案与基准模型进行对比，验证了本方法的增量价值。结果表明，该问题可通过我们提出的贝叶斯非齐次泊松过程实现更高精度的成功建模，为自动化碰撞规避系统的发展提供支撑，帮助运营者及时且节制地执行卫星机动操作。