Abstract
Maintaining a catalog of Resident Space Objects (RSOs) can be cast in a typical Bayesian multi-object estimation problem, where the various sources of uncertainty in the problem – the orbital mechanics, the kinematic states of the identified objects, the data sources, etc. – are modeled as random variables with associated probability distributions. In the context of Space Situational Awareness, however, the information available to a space analyst on many uncertain components is scarce, preventing their appropriate modeling with a random variable and thus their exploitation in a RSO tracking algorithm. A typical example are human-based data sources such as Two-Line Elements (TLEs), which are publicly available but lack any statistical description of their accuracy and precision. In this paper, we propose the first exploitation of uncertain variables in a RSO tracking problem, allowing for a representation of the uncertain components reflecting the information available to the space analyst, however scarce, and nothing more. In particular, we show that a human-based data source and a physics-based data source can be embedded in a unified and rigorous Bayesian estimator in order to track a RSO. We illustrate this concept on a scenario where real TLEs queried from the U.S. Strategic Command are fused with realistically simulated radar observations in order to track a Low-Earth Orbit satellite.

Bio
Dr Moriba Jah is an Associate Professor in the Aerospace Engineering and Engineering Mechanics Department, Cockrell School of Engineering, The University of Texas at Austin. He leads a research program called ASTRIA that uses expertise in Astrodynamics, Sensors Systems, and Estimation Theory focused on solving problems in Spacecraft Navigation, Space Situational Awareness, Space Security, and Space Traffic Management. His prior experience includes being a spacecraft navigator at JPL and senior researcher at the Air Force Research Lab.