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For objects in the low Earth orbit region, uncertainty in atmospheric densityestimation is an important source of orbit prediction error, which is criticalfor space situational awareness activities such as the satellite conjunctionanalysis. This paper investigates the evolution of orbit error distribution inthe presence of atmospheric density uncertainties, which are modeled usingprobabilistic machine learning techniques. The recently proposed HASDM-ML,CHAMP-ML, and MSIS-UQ machine learning models for density estimation are usedin this work. The investigation is convoluted because of the spatial andtemporal correlation of the atmospheric density values. We develop severalMonte Carlo methods, each capturing a different spatiotemporal densitycorrelation, to study the effects of density uncertainty on orbit uncertaintypropagation. However, Monte Carlo analysis is computationally expensive, so afaster method based on the Kalman filtering technique for orbit uncertaintypropagation is also explored. It is difficult to translate the uncertainty inatmospheric density to the uncertainty in orbital states under a standardextended Kalman filter or unscented Kalman filter framework. This work uses theso-called consider covariance sigma point (CCSP) filter that can account forthe density uncertainties during orbit propagation. As a test-bed forvalidation purposes, a comparison between CCSP and Monte Carlo methods of orbituncertainty propagation is carried out. Finally, using the HASDM-ML, CHAMP-ML,and MSIS-UQ density models, we propose an ensemble approach for orbituncertainty quantification for four different space weather conditions.

Advanced ensemble modeling method for space object state prediction accounting for uncertainty in atmospheric density