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Collision Avoidance Challenge (2019)

Deep Learning Approach

Comptetition organized by European Space Agency (ESA)

Link to competition

Today, active collision avoidance among orbiting satellites has become a routine task in space operations, relying on validated, accurate and timely space surveillance data. For a typical satellite in Low Earth Orbit, hundreds of alerts are issued every week corresponding to possible close encounters between a satellite and another space object (in the form of conjunction data messages CDMs). After automatic processing and filtering, there remain about 2 actionable alerts per spacecraft and week, requiring detailed follow-up by an analyst. On average, at the European Space Agency, more than one collision avoidance manoeuvre is performed per satellite and year.

In this challenge, you are tasked to build a model to predict the final collision risk estimate between a given satellite and a space object (e.g. another satellite, space debris, etc). To do so, you will have access to a database of real-world conjunction data messages (CDMs) carefully prepared at ESA.

Project developed with Sergio Pérez

Deep Learning Approaches

  • Fully Connected Network
  • Convolutional Neural Networks
  • Recurrent Neural Networks
  • Autoecoder Network
  • Siamese Network (Manhattan LSTM)

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