Associating optical measurements of MEO and GEO objects using Population-Based Meta-Heuristic methods

Zittersteijn, Michiel; Vananti, Alessandro; Schildknecht, Thomas; Dolado Perez, J.C.; Martinot, V. (2016). Associating optical measurements of MEO and GEO objects using Population-Based Meta-Heuristic methods. Advances in space research, 58(9), pp. 1778-1792. Elsevier 10.1016/j.asr.2016.06.026

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Currently several thousands of objects are being tracked in the MEO and GEO regions through optical means. The problem faced in this framework is that of Multiple Target Tracking (MTT). The MTT problem quickly becomes an NP-hard combinatorial optimization problem. This means that the effort required to solve the MTT problem increases exponentially with the number of tracked objects. In an attempt to find an approximate solution of sufficient quality, several Population-Based Meta-Heuristic (PBMH) algorithms are imple- mented and tested on simulated optical measurements. These first results show that one of the tested algorithms, namely the Elitist Genetic Algorithm (EGA), consistently displays the desired behavior of finding good approximate solutions before reaching the optimum. The results further suggest that the algorithm possesses a polynomial time complexity, as the computation times are consistent with a polynomial model. With the advent of improved sensors and a heightened interest in the problem of space debris, it is expected that the number of tracked objects will grow by an order of magnitude in the near future. This research aims to provide a method that can treat the association and orbit determination problems simultaneously, and is able to efficiently process large data sets with minimal manual intervention.

Item Type:

Journal Article (Original Article)


08 Faculty of Science > Institute of Astronomy

UniBE Contributor:

Zittersteijn, Michiel; Vananti, Alessandro and Schildknecht, Thomas


500 Science > 520 Astronomy








Alessandro Vananti

Date Deposited:

23 Jan 2017 12:09

Last Modified:

24 Jan 2017 09:05

Publisher DOI:





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