Orbit determination of space objects based on sparse optical data
A. Milani, G. Tommei, D. Farnocchia, A. Rossi, T. Schildknecht, R., Jehn

TL;DR
This paper demonstrates that sparse optical observations can be effectively correlated and used for orbit determination of GEO space objects, reducing the need for extensive telescope networks.
Contribution
It introduces and validates two algorithms for correlating sparse observations and determining orbits, including non-gravitational effects, for GEO objects.
Findings
Successful orbit determination from sparse data sets
Reduction in telescope network requirements
Validation with real ESA Space Debris Telescope data
Abstract
While building up a catalog of Earth orbiting objects, if the available optical observations are sparse, not deliberate follow ups of specific objects, 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), including the accurate orbit determination by full least squares solutions with all six orbital elements. Because of the presence in the GEO region of a significant subpopulation of high area to mass objects, strongly affected by non-gravitational perturbations, it was actually necessary to solve also for…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
