Hybrid Perturbation methods based on Statistical Time Series models
Juan F\'elix San-Juan, Montserrat San-Mart\'in, Iv\'an P\'erez,, Rosario L\'opez

TL;DR
This paper introduces a hybrid orbit propagation methodology combining analytical models with statistical time series predictions to enhance accuracy in satellite position and velocity calculations.
Contribution
It presents a novel hybrid perturbation theory that integrates analytical orbit models with statistical time series techniques for improved orbit prediction accuracy.
Findings
Hybrid models outperform traditional methods in accuracy.
Validation with Earth's flattening effects shows improved results.
Different orders of analytical approximations tested for effectiveness.
Abstract
In this work we present a new methodology for orbit propagation, the hybrid perturbation theory, based on the combination of an integration method and a prediction technique. The former, which can be a numerical, analytical or semianalytical theory, generates an initial approximation that contains some inaccuracies derived from the fact that, in order to simplify the expressions and subsequent computations, not all the involved forces are taken into account and only low-order terms are considered, not to mention the fact that mathematical models of perturbations not always reproduce physical phenomena with absolute precision. The prediction technique, which can be based on either statistical time series models or computational intelligence methods, is aimed at modelling and reproducing missing dynamics in the previously integrated approximation. This combination results in the precision…
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