Associating optical measurements and estimating orbits of geocentric objects through population-based meta-heuristic methods

Zittersteijn, Michiel; Vananti, Alessandro; Schildknecht, Thomas; Dolado-Perez, J.C.; Martinot, V. (2015). Associating optical measurements and estimating orbits of geocentric objects through population-based meta-heuristic methods. In: Proceedings of 66th International Astronautical Congress. Curran Associates, Inc.

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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). In this context both, the correct
associations among the observations and the orbits of the objects have to be determined. The complexity of the MTT problem is defined by its dimension S.
The number S corresponds to the number of fences involved in the problem.
Each fence consists of a set of observations where each observation belongs to a different object. The S ≥ 3 MTT problem is an NP-hard combinatorial optimization problem. There are two general ways to solve this. One way is to
seek the optimum solution, this can be achieved by applying a branch-and-
bound algorithm. When using these algorithms the problem has to be greatly simplified to keep the computational cost at a reasonable level. Another
option is to approximate the solution by using meta-heuristic methods. These methods aim to efficiently explore the different possible combinations so that a reasonable result can be obtained with a reasonable computational effort. To this end several population-based meta-heuristic methods are implemented and tested on simulated optical measurements. 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 correlation and orbit determination problems simultaneously, and is able to efficiently process large data sets with minimal manual intervention.

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