A deterministic approach to active debris removal target selection

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

[1]  James Agi Woodburn.  A Description of Filters for Minimizing the Time Required for Orbital Conjunction Computations , 2009 .

[2]  Russell P. Patera.  Space Event Detection Method , 2008 .

[3]  Max Cerf,et al.  Multiple Space Debris Collecting Mission—Debris Selection and Trajectory Optimization , 2011, J. Optim. Theory Appl..

[4]  Salvatore Alfano.  Determining satellite close approaches, part 2 , 1994 .

[5]  Felix R. Hoots,et al.  SPACETRACK REPORT NO. 3 Models for Propagation of , 1988 .

[6]  Adam E. White,et al.  The many futures of active debris removal , 2014 .

[7]  Vincent Agi Coppola.  Determination of Close Approaches Based on Ellipsoidal Threat Volumes , 1999 .

[8]  G. Roberts.  Satellite Tracking Software , 2007 .

[9]  Heiner Klinkrad,et al.  Collision Risk Assessment with a `Smart Sieve' Method , 2002 .

[10]  Linda L. Crawford,et al.  An analytic method to determine future close approaches between satellites , 1984 .

[11]  Holger Krag,et al.  ESA’s process for the identification and assessment of high-risk conjunction events , 2009 .

[12]  Darren S. McKnight,et al.  System engineering analysis of derelict collision prevention options , 2013 .

[13]  Liam M. Healy,et al.  Close conjunction detection on parallel computer , 1995 .

[14]  Waldemar Bauer,et al.  Target selection and comparison of mission design for space debris removal by DLR׳s advanced study group ☆ , 2014 .

[15]  J. Liou.  Collision activities in the future orbital debris environment , 2004 .

[16]  D. Kessler,et al.  Collision frequency of artificial satellites: The creation of a debris belt , 1978 .

[17]  V. F. Baik. Av,et al.  Direct method for the analysis of collision probability of artificial space objects in LEO: techniques, results and applications , 1993 .

[18]  J. Liou.  An active debris removal parametric study for LEO environment remediation , 2011 .

[19]  N. Johnson,et al.  Instability of the Present LEO Satellite Populations , 2008 .

[20]  Victor P Osweiler,et al.  Covariance Estimation and Autocorrelation of NORAD Two-Line Element Sets , 2012 .

[21]  Nicolas Bérend,et al.  Estimation of the probability of collision between two catalogued orbiting objects , 1999 .

[22]  Ken Chan.  International Space Station Collision Probability , 2013 .

[23]  Holger Krag,et al.  Assessment and Categorization of TLE Orbit Errors for the US SSN Catalogue , 2008 .

[24]  Salvatore Alfano,et al.  Determining satellite close approaches , 1992 .