Tim Flohrer, ESA/ESOC Space Debris Office; Holger Krag, ESA Space Debris Office; Klaus Merz, ESA Space Debris Office; Stijn Lemmens, ESA/ESOC Space Debris Office;
Keywords: Collision Avoidance, SSA, Space Safety, Automation, Large constellations
Abstract:
Today, active collision avoidance has become a routine task in space operations, relying on validated, accurate and timely space surveillance data. For a typical satellite in LEO hundreds of conjunction alerts can be expected every week. Processing and filtering these still leaves about 2 actionable alerts per spacecraft and week requiring detailed follow-up by an analyst. At ESA more than one collision avoidance manoeuvre can be expected per satellite and year. It is clear that such an approach requiring 24/7 expert availability to analyse more than 20 parameters and constraints generates high operational costs. The future with an accelerating launch rate and deployments of smaller satellites and constellation, as well as improved space surveillance networks delivering catalogues of up to 200k objects will render efforts by any operator following this approach an unmanageable task.
ESA’s proposal for a Space Safety Programm to start in 2020 also includes a cornerstone “Collision Risk Estimation and Automated Mitigation (CREAM)”. CREAM entails the development of technologies for automating collision avoidance and its demonstration with a suitable newly developed or existing flying platform.
We will discuss the status of the proposal focussing on three central objectives: (a) reducing operator efforts in particular for large constellations, (b) reducing the number of false alerts, (c) reducing the time between decision and close approach. In our discussion we will discuss ideas for machine learning techniques to replicate expert decisions. We will discuss their application to automatic handling and analysis of the reliability of collision risk estimates, as well as implications from emerging trends in spacecraft operations, such as enhancing the visibility for surveillance networks, low-thrust manoeuvring or attitude changes to control the effective drag towards a continuous collision avoidance process in replacement of the classical impulsive manoeuvring.
An efficient way to coordinate and command manoeuvres is needed for the success of the CREAM concept. We will introduce first conceptual ideas how, e.g., adapting an IoT scenario could be implemented to enable late decisions on collision avoidance actions, also considering on-board trajectory estimation based on GNSS and ground-based orbit refinements.
The presentation will conclude with an outlook to possible demonstration scenarios for the key technology developments, either with newly developed platform, or by using existing missions.
Date of Conference: September 17-20, 2019
Track: Space Situational Awareness