  A new multifidelity method is developed for nonlinear orbit uncertainty
propagation. This approach guarantees improved computational efficiency and
limited accuracy losses compared to fully high-fidelity counterparts. The
initial uncertainty is modeled as a weighted sum of Gaussian distributions
whose number is adapted online to satisfy the required accuracy. As needed,
univariate splitting libraries are used to split the mixture components along
the direction of maximum nonlinearity. Differential Algebraic techniques are
used to propagate these Gaussian kernels and compute a measure of nonlinearity
required for the split decision and direction identification. Taylor expansions
of the flow of the dynamics are computed using a low-fidelity dynamical model
to maximize computational efficiency and corrected with selected high-fidelity
samples to minimize accuracy losses. The effectiveness of the proposed method
is demonstrated for different dynamical regimes combining SGP4 theory and
numerical propagation as low- and high-fidelity models respectively.
