Physics and Human-Based Information Fusion for Improved Resident Space Object Tracking.

Maintaining a catalog of Resident Space Objects (RSOs) can be cast in atypical Bayesian multi-object estimation problem, where the various sources ofuncertainty in the problem - the orbital mechanics, the kinematic states of theidentified objects, the data sources, etc. - are modeled as random variableswith associated probability distributions. In the context of Space SituationalAwareness, however, the information available to a space analyst on manyuncertain components is scarce, preventing their appropriate modeling with arandom variable and thus their exploitation in a RSO tracking algorithm. Atypical example are human-based data sources such as Two-Line Elements (TLEs),which are publicly available but lack any statistical description of theiraccuracy. In this paper, we propose the first exploitation of uncertainvariables in a RSO tracking problem, allowing for a representation of theuncertain components reflecting the information available to the space analyst,however scarce, and nothing more. In particular, we show that a human-baseddata source and a physics-based data source can be embedded in a unified andrigorous Bayesian estimator in order to track a RSO. We illustrate this concepton a scenario where real TLEs queried from the U.S. Strategic Command are fusedwith realistically simulated radar observations in order to track a Low-EarthOrbit satellite.