Abstract: The present disclosure relates to the quantification and propagation of orbital state uncertainties and accurately determining an orbit state of an orbiting object using multi-model ensemble analysis. Input data (e.g., solar indices, geomagnetic indices, space weather parameters, temporal parameters, etc.) associated with the solar environment and orbiting object can be provided as inputs to multiple trained density prediction models. The trained density prediction models can be configured to output atmospheric density data associated with the orbiting object (e.g., satellite). Using orbit propagation for the respective atmospheric density data, orbit data (e.g., position, velocity) can be predicted. The predicted orbit data associated with the multiple density prediction models can then be analyzed in an ensemble approach to accurately predict the orbit state of the orbiting object.

Abstract: The present disclosure relates to an upper-atmospheric mass density prediction model with robust and reliable uncertainty estimates in accordance with various embodiments of the present disclosure. The upper-atmospheric mass density model is developed based on the SET HASDM density database. In various embodiments, PCA is used to reduce the spatial dimension of the dataset. The input sets used to train the mass density model contains a time series for the geomagnetic indices. The mass density prediction model is trained to output a mass density map for accurately prediction trajectories of satellites. For example, a likelihood of collision associated with a given object can be determined based at least in part on the mass density map. Analysis of the mass density map along with the likelihood of collision can used to determine a trajectory for the given object in space.

Abstract: This disclosure describes techniques for providing a transformative framework to forecast physical properties of an atmosphere to predict the orbit of satellite devices. As one example, the transformative framework has two major components: (i) the development of a quasi-physical dynamic reduced-order model (ROM) that uses a linear approximation of the underlying dynamics (e.g., solar conditions or magnetic conditions) and effect of the drivers, and (ii) data assimilation and calibration of the ROM through estimation of the ROM coefficients that represent the model parameters.