Towards Automated Satellite Conjunction Management with Bayesian Deep Learning
This work is significant for satellite operators and space agencies by providing automated tools to predict potential collision events, which is crucial for managing the growing risk of space debris and maintaining critical satellite infrastructure.
The paper addresses the challenge of predicting satellite conjunction events to prevent collisions in low Earth orbit, which is increasingly burdened by space debris and mega-constellations. The authors developed a Bayesian deep learning approach using recurrent neural networks (LSTMs) to model time series of conjunction data messages (CDMs), enabling simultaneous prediction of all CDM features, including future CDM arrival times, along with associated uncertainties.
After decades of space travel, low Earth orbit is a junkyard of discarded rocket bodies, dead satellites, and millions of pieces of debris from collisions and explosions. Objects in high enough altitudes do not re-enter and burn up in the atmosphere, but stay in orbit around Earth for a long time. With a speed of 28,000 km/h, collisions in these orbits can generate fragments and potentially trigger a cascade of more collisions known as the Kessler syndrome. This could pose a planetary challenge, because the phenomenon could escalate to the point of hindering future space operations and damaging satellite infrastructure critical for space and Earth science applications. As commercial entities place mega-constellations of satellites in orbit, the burden on operators conducting collision avoidance manoeuvres will increase. For this reason, development of automated tools that predict potential collision events (conjunctions) is critical. We introduce a Bayesian deep learning approach to this problem, and develop recurrent neural network architectures (LSTMs) that work with time series of conjunction data messages (CDMs), a standard data format used by the space community. We show that our method can be used to model all CDM features simultaneously, including the time of arrival of future CDMs, providing predictions of conjunction event evolution with associated uncertainties.