While building up a catalogue of Earth-orbiting objects, the available optical observations are typically sparse. In this case, no orbit determination is possible without previous correlation of observations obtained at different times. This correlation step is the most computationally intensive, and becomes more and more difficult as the number of objects to be discovered increases. In this paper, we tested two different algorithms, and the related prototype software, recently developed to solve the correlation problem for objects in geostationary orbit (GEO). The algorithms allow the accurate orbit determination by full least-squares solutions with all six orbital elements. The presence of a significant subpopulation of high area-to-mass ratio objects in the GEO region, strongly affected by non-gravitational perturbations, required to solve also for dynamical parameters describing these effects, that is to fit between six and eight free parameters for each orbit.
The validation was based upon a set of real data, acquired from the European Space Agency (ESA) Space Debris Telescope (ESASDT) at the Teide Observatory (Canary Islands). We proved that it is possible to assemble a set of sparse observations into a set of objects with orbits. This would allow a survey strategy covering the region of interest in the sky just once per night. As a result, it would be possible to significantly reduce the requirements for a future telescope network, with respect to what would have been required with the previously known algorithms for correlation and orbit determination.