Improving orbital uncertainty realism through covariance determination

Abstract

Classical Orbit Determination algorithms provide an estimation of the state vector and its associated uncertainty assuming Gaussian processes and linear dynamics. However, a common problem of Orbit Determination processes is the misrepresentation of the Resident Space Object uncertainty through the estimated and predicted covariance. Ultimately, this causes a great impact in the quality and accuracy of Space Surveillance and Tracking products as the covariance is overly optimistic (too small) and the true uncertainty of the object is not properly captured. One of the causes for the unrealism of the covariance is found in the uncertainty of the dynamical and measurement models used to describe the motion of the objects.The aim of this work is to devise a novel methodology to improve the covariance realism of Orbit Determination and Orbit Propagation processes through the classical theory of consider parameters in batch least-squares estimators. The devised methodology uses the theory of consider parameters to add the uncertainty of dynamic and measurement models to the estimated covariance.The magnitude of the consider parameters is weighted using statistical inference techniques, proposing an innovative method to derive its contribution to the covariance. The conceived methodology is suitable for any type of measurement or object, although its primary goal is to correct unrealistic covariance of non-cooperative targets.Among the wide variety of uncertainty sources affecting covariance realism, the influence and effect of two relevant modelling uncertainties, the atmospheric drag force and the range bias, is discussed during this publication. The proposed methodology is first applied to a simulated scenario of tracking measurements, using a Monte Carlo approach. A case involving real radar observations is presented as well, where the uncertainty realism of the Sentinel-3A orbit determination and propagation is assessed and improved. Using the novel methodology of Covariance Determination, covariance realism improvement of a real tracking campaign is achieved and the uncertainty of the atmospheric drag force model quantified.