论文标题
航天器碰撞风险评估与概率编程
Spacecraft Collision Risk Assessment with Probabilistic Programming
论文作者
论文摘要
超过34,000个物体大于10厘米的长度是轨道地球的已知。其中,只有一小部分是活跃的卫星,而其余的人口则由死去的卫星,火箭尸体和碎屑制成,这些卫星对飞船构成了碰撞威胁。此外,预测太空行业的增长以及计划的巨型构成将增加更复杂的性能,从而导致碰撞风险和空间运营商的负担增加。通过国际商定的方法管理这个复杂的框架是关键和紧迫的。在这种情况下,我们构建了一种基于物理学的新型概率生成模型,用于合成生成连词数据消息,并使用真实数据进行校准。通过对观测来调节,我们使用该模型通过贝叶斯推断获得后验分布。我们表明,结合评估的概率编程方法可以帮助做出预测,并找到在连词数据消息中解释观察到的数据的参数,从而更多地了解关键变量和轨道特征,从而更有可能导致连接事件。此外,我们的技术可以生成碰撞的物理准确的合成数据集,从而回答了在该领域工作的太空和机器学习社区的基本需求。
Over 34,000 objects bigger than 10 cm in length are known to orbit Earth. Among them, only a small percentage are active satellites, while the rest of the population is made of dead satellites, rocket bodies, and debris that pose a collision threat to operational spacecraft. Furthermore, the predicted growth of the space sector and the planned launch of megaconstellations will add even more complexity, therefore causing the collision risk and the burden on space operators to increase. Managing this complex framework with internationally agreed methods is pivotal and urgent. In this context, we build a novel physics-based probabilistic generative model for synthetically generating conjunction data messages, calibrated using real data. By conditioning on observations, we use the model to obtain posterior distributions via Bayesian inference. We show that the probabilistic programming approach to conjunction assessment can help in making predictions and in finding the parameters that explain the observed data in conjunction data messages, thus shedding more light on key variables and orbital characteristics that more likely lead to conjunction events. Moreover, our technique enables the generation of physically accurate synthetic datasets of collisions, answering a fundamental need of the space and machine learning communities working in this area.