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