Predicting the Probability of Collision of a Satellite with Space
  Debris: A Bayesian Machine Learning Approach
João Simões Catulo Cláudia Soares Marta Guimarães
Space is becoming more crowded in Low Earth Orbit due to increased space
activity. Such a dense space environment increases the risk of collisions
between space objects endangering the whole space population. Therefore, the
need to consider collision avoidance as part of routine operations is evident
to satellite operators. Current procedures rely on the analysis of multiple
collision warnings by human analysts. However, with the continuous growth of
the space population, this manual approach may become unfeasible, highlighting
the importance of automation in risk assessment. In 2019, ESA launched a
competition to study the feasibility of applying machine learning in collision
risk estimation and released a dataset that contained sequences of Conjunction
Data Messages (CDMs) in support of real close encounters. The competition
results showed that the naive forecast and its variants are strong predictors
for this problem, which suggests that the CDMs may follow the Markov property.
The proposed work investigates this theory by benchmarking Hidden Markov Models
(HMM) in predicting the risk of collision between two resident space objects by
using one feature of the entire dataset: the sequence of the probability in the
CDMs. In addition, Bayesian statistics are used to infer a joint distribution
for the parameters of the models, which allows the development of robust and
reliable probabilistic predictive models that can incorporate physical or prior
knowledge about the problem within a rigorous theoretical framework and
provides prediction uncertainties that nicely reflect the accuracy of the
predicted risk. This work shows that the implemented HMM outperforms the naive
solution in some metrics, which further adds to the idea that the collision
warnings may be Markovian and suggests that this is a powerful method to be
further explored.