Improving Orbital Uncertainty Realism Through Covariance Determination in GEO

• Published in: The Journal of the astronautical sciences Vol. 69; no. 5; pp. 1394 - 1420
• Main Authors: Cano, Alejandro, Pastor, Alejandro, Fernández, Sergio, Míguez, Joaquín, Sanjurjo-Rivo, Manuel, Escobar, Diego
• Format: Journal Article
• Language: English
• Published: New York Springer US 2022; Springer Nature B.V
• Subjects: Advanced Maui Optical and Space Surveillance Technologies (AMOS 2021); Aerospace Technology and Astronautics; Chi-square test; Covariance; Dynamic models; Engineering; Mathematical Applications in the Physical Sciences; Normal distribution; Orbit determination; Orbits; Original Article; Parameters; Realism; Situational awareness; Space Exploration and Astronautics; Space Sciences (including Extraterrestrial Physics; Uncertainty; Variance; Covariance realism; Uncertainty realism; Chi-square distribution; Mahalanobis distance; Covariance determination; Space situational awareness
• ISSN: 2195-0571; 0021-9142; 2195-0571
• DOI: 10.1007/s40295-022-00343-x

The reliability of the uncertainty characterization, also known as uncertainty realism, is of the uttermost importance for Space Situational Awareness (SSA) services. Among the different sources of uncertainty related to the orbits of Resident Space Objects (RSOs), the uncertainty of dynamic models is one of the most relevant ones, although it is not always included in orbit determination processes. A classical approach to account for these sources of uncertainty is the consider parameters theory, which consists in including parameters in the underlying dynamical models whose variance aims to represent the uncertainty of the system. However, realistic variances of these consider parameters are not known a-priori. This work presents a method to infer the variance of the consider parameters, based on the distribution of the Mahalanobis distance of the orbital differences between predicted and estimated orbits, which theoretically shall follow a χ 2 distribution under Gaussian assumption. This paper presents results in a simulated scenario focusing on Geostationary (GEO) regimes. The effectiveness and traceability of the uncertainty sources is assessed via covariance realism metrics.