Optimizing Consider-Covariance Models for Realistic Orbit Errors and Application to Collision Avoidance

Abstract

Precise knowledge about the orbit state vectors and the associated uncertainties are crucial for reliable conjunction assessment and efficient mitigation of on-orbit collision risks through collision avoidance maneuvers by active satellites. The decision-making process heavily relies on the orbit accuracy and the uncertainty realism of the encountering objects, primarily space debris, and the maneuvering satellite itself. The process of determining an orbit creates an estimate of the orbit state and its associated state covariance matrix. This estimate serves as the initial value for predicting the orbit’s uncertainty at the time of closest approach. To determine and numerically propagate realistic orbit errors, the uncertainties of the dynamic model must be taken into account. One method that fulfils this purpose is consider covariance propagation, provided that suitable consider parameters and their variances have been established. This paper presents a novel method for optimizing the consider covariance parameters from past mission data. Real orbit data from multiple low Earth orbit satellites controlled by the DLR, German Aerospace Center’s German Space Operations Center are used to calibrate and evaluate different error models, including covariance parameters and other error terms. The results demonstrate improved uncertainty realism in orbit predictions and subsequent benefits for collision avoidance. Finally, this paper presents the required steps for integrating the new method into the existing collision avoidance system, along with initial results from conjunction assessment.