Physics and Human-Based Information Fusion for Improved Resident Space Object Tracking
Emmanuel Delande, Jeremie Houssineau, Moriba Jah

TL;DR
This paper introduces a novel Bayesian fusion approach that combines physics-based data and human-sourced information, like TLEs, to improve Resident Space Object tracking with scarce data.
Contribution
It is the first to incorporate uncertain variables representing scarce human-based data sources into a unified Bayesian RSO tracking framework.
Findings
Fusion of TLEs and radar data improves tracking accuracy.
Uncertain variables effectively model scarce human-based data.
Method demonstrates practical application with real TLEs and simulated radar data.
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. In this paper, we propose the first exploitation of uncertain variables in a RSO tracking problem, allowing for a…
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