Usage of Conjunction Data Messages to infer orbit errors of Special Perturbation catalogued objects

Abstract

In the last decades, the Earth-orbiting population of both active and non-active objects has grown significantly, leading to a substantial increase in number of possible in-orbit collisions. It is therefore crucial to constantly monitor the orbit of space resident objects to assess in advance the threat of break-up events. Within this framework, the Joint Space Operation Centre (JSpOC) is consistently updating the orbit of thousands of tracked objects by processing observations of the U.S. Space Surveillance Network (SSN). The determined orbital data is continuously maintained in the Special Perturbation (SP) catalogue and used to issue close approach warnings to satellite operators around the globe under the form of Conjunction Data Messages (CDM). The Flight Dynamics (FD) group of the German Space Operation Centre (GSOC) receives on regular basis a subset of the SP catalogue data along with CDMs associated to the fleet of its controlled satellites. The SP ephemerides are in fact provided without any covariance information preventing any collision risk assessment. In GSOC FD we are implementing a service to link a series of synthetic orbital error covariance matrices to a given SP ephemeris by statistically analyzing historical CDM of past events. More than 16 GB of past conjunction data are processed to extract state vector, covariance matrix and Radar Cross Section (RCS) of already encountered secondary objects. The orbital errors of these last are subsequently categorized and divided into orbital classes to decouple the high correlation the covariance has from, solar flux, object dimension, altitude of perigee, eccentricity and orbit inclination. The classification aims to collect similar CDMs regarding the aforementioned dependencies, and approximate the predicted 1-sigma position errors in orbital frame by optimal curve-fitting techniques. By evaluation of the curve fitting coefficients of a requested orbit class a covariance matrix can be generated for any prediction time in upcoming CDM refinements and other analysis. The presentation introduces a performance check of the method by comparing the synthetically generated timeline of orbital errors against real data of flying satellites belonging to the same orbital class. The limiting cases of the orbital classification approach are also discussed, bringing possible solutions to the scenario of empty classes. The CDM data coverage, and thus the subsequent statistical analysis, is bounded only to the orbital regimes of the primaries upon which the dataset is built. Lastly, some direct applications and possible usage of the implemented tool are shown. The covariance retrieval method can allow conjunction risk assessment capabilities to Space Situational Awareness catalogues that are integrating external data sources because of their limited sensor network capacity.