A deterministic approach to active debris removal target selection

Abstract

Many decisions, with widespread economic, political and legal consequences, are being considered based on the concerns about the sustainability of spaceflight and space debris simulations that show that Active Debris Removal (ADR) may be necessary.
The debris environment predictions are affected by many sources of error, including low-accuracy ephemerides and propagators. This, together with the inherent unpredictability of e.g. solar activity or debris attitude, raises doubts about the ADR target-lists that are produced. Target selection is considered highly important, as removal of non-relevant objects will unnecessarily increase the overall mission cost [1].
One of the primary factors that should be used in ADR target selection is the accumulated collision probability of every object [2]. To this end, a conjunction detection algorithm, based on the “smart sieve” method, has been developed and utilised with an example snapshot of the public two-line element catalogue. Another algorithm was then applied to the identified conjunctions to estimate the maximum and true probabilities of collisions taking place.
Two target-lists were produced based on the ranking of the objects according to the probability they will take part in any collision over the simulated time window. These probabilities were computed using the maximum probability approach, which is time-invariant, and estimates of the true collision probability that were computed with covariance information.
The top-priority targets are compared, and the impacts of the data accuracy and its decay highlighted. General conclusions regarding the importance of Space Surveillance and Tracking for the purpose of ADR are drawn and a deterministic method for ADR target selection, which could reduce the number of ADR missions to be performed, is proposed

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ORCID for Hugh G. Lewis: orcid.org/0000-0002-3946-8757

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