Towards automated satellite conjunction management with Bayesian deep learning

Francesco Pinto, Giacomo Acciarini, Sascha Metz, Sarah Boufelja, Sylvester Kaczmarek, Klaus Merz, José A. Martinez-Heras, Francesca Letizia, Christopher Bridges, Atılım Güneş Baydin

Research output: Contribution to conferencePaperpeer-review

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.
Original languageEnglish
Number of pages7
Publication statusPublished - 6 Dec 2020
EventNeurIPS 2020: AI for Earth Sciences Workshop - Canada, Vancouver
Duration: 6 Dec 202012 Dec 2020
https://ai4earthscience.github.io/neurips-2020-workshop/

Workshop

WorkshopNeurIPS 2020
CityVancouver
Period6/12/2012/12/20
Internet address

Keywords

  • satellite conjunction management
  • spacecraft collision risk
  • bayesian deep learning
  • artificial intelligence (AI)
  • machine learning (ML)
  • long-short term memory networks
  • conjunction data messages
  • kessler syndrome

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