Abstract
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.