Uncertainty Propagation Using Hybrid Methods

TL;DR

This paper introduces a hybrid orbit propagation method that enhances the classical SGP4 model by incorporating uncertainty propagation through a state-space exponential smoothing approach, improving short-term forecast accuracy.

Contribution

The paper presents a novel hybrid SGP4 orbit propagator that integrates uncertainty modeling with exponential smoothing, providing improved short-term prediction accuracy.

Findings

Enhanced accuracy for short-term orbit forecasts.

Effective incorporation of Gaussian noise in model fitting.

Improved confidence intervals for orbit predictions.

Abstract

Small corrections in the argument of the latitude can be used to improve the accuracy of the SGP4 orbit propagator. These corrections have been obtained by applying the hybrid methodology for orbit propagation to SGP4, therefore yielding a hybrid version of this propagator. The forecasting part of the hybrid method is based on a state-space formulation of the exponential smoothing method. If the error terms that have to be considered during the model fitting process are taken as Gaussian noise, then the maximum-likelihood method can be applied so as to estimate the parameters of the exponential-smoothing model, as well as to compute the forecast together with its confidence interval. Finally, this hybrid SGP4 orbit propagator has been applied to data from Galileo-type orbits. This new propagator improves the accuracy of the classical SGP4, especially for short forecasting horizons.