Application of Computational Intelligence in Order to Develop Hybrid Orbit Propagation Methods

Pérez, Iván; San-Juan, Juan F.; San-Martín, Montserrat; Ochoa, Luis María López

doi:10.1155/2013/631628

Cited by 13 publications

(6 citation statements)

References 26 publications

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“…The aforementioned simplifications and inaccuracies in the integration methods lead to solutions that are approximate, and consequently can be improved. In order to achieve it, forecasting techniques based on either statistical time series models [16,17,18] or machine learning methods [19,20] can be used. They can model the dynamics of the difference between the integrated approximate solution and the real behavior, during an initial control interval, with the aim of reproducing it later, when real ephemerides are no longer available.…”

Section: Introductionmentioning

confidence: 99%

Hybrid SGP4 orbit propagator

et al. 2017

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Two-Line Elements (TLEs) continue to be the sole public source of orbiter observations. The accuracy of TLE propagations through the Simplified General Perturbations-4 (SGP4) software decreases dramatically as the propagation horizon increases, and thus the period of validity of TLEs is very limited. As a result, TLEs are gradually becoming insufficient for the growing demands of Space Situational Awareness (SSA). We propose a technique, based on the hybrid propagation methodology, aimed at extending TLE validity with minimal changes to the current TLE-SGP4 system in a non-intrusive way. It requires that the institution in possession of the osculating elements distributes hybrid TLEs, HTLEs, which encapsulate the standard TLE and the model of its propagation error. The validity extension can be accomplished when the end user processes HTLEs through the hybrid SGP4 propagator, HSGP4, which comprises the standard SGP4 and an error corrector.Comment: 18 pages, 4 figure

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Section: Introductionmentioning

confidence: 99%

Hybrid SGP4 orbit propagator

et al. 2017

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“…In this work we present the hybrid perturbation theory, which may combine any kind of the aforementioned integration techniques with forecasting techniques based on statistical time series models (Chan and Ripley, 2012;Trapletti et al, 2015;San-Juan et al, 2012) or computational intelligence methods (Pérez et al, 2013). This combination allows for an increase in the accuracy of the numerical, analytical or semianalytical theories for predicting the position and velocity of any artificial satellite or space debris object, through the modelling of higher-order terms and other external forces not considered in those initial theories, as well as some physical effects not accurately modelled by the mathematical equations.…”

Section: Introductionmentioning

confidence: 99%

Hybrid perturbation methods based on statistical time series models

San-Juan

San-Martín

Pérez

et al. 2016

Advances in Space Research

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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 pre- * Corresponding author. Tel.: 34 941299440; fax: 34 941299460. cision improvement of conventional numerical, analytical and semianalytical theories for determining the position and velocity of any artificial satellite or space debris object. In order to validate this methodology, we present a family of three hybrid orbit propagators formed by the combination of three different orders of approximation of an analytical theory and a statistical time series model, and analyse their capability to process the effect produced by the flattening of the Earth. The three considered analytical components are the integration of the Kepler problem, a first-order and a second-order analytical theories, whereas the prediction technique is the same in the three cases, namely an additive Holt-Winters method.

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“…Various machine learning methods and hybrid methods are quite successfully used for solving problems of propagating and predicting the motion of space objects, among them neural network methods, Kalman filter and support vector method. In [3], a neural network was used to increase the accuracy of the analytical predictor. As a result, a combination of both methods reduces the error in calculating the position of a space object and improves the accuracy of predicting its motion.…”

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Autoregression Models of Space Objects Movement Represented by Tle Elements

Sarichev¹,

Perviy²

2020

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Annotation. The developed method, which is a modification of the previously developed methods for constructing autoregressive models, is used to simulate the motion of space objects in the time series of their TLE elements. The modeling system has been developed that includes: determining the optimal volume of training samples in modeling time series of TLE elements; determination of the autoregression order for each variable (TLE element); determination of the optimal structure and identification of the parameters of the autoregressive model for each variable; identification of patterns of evolution of the mean square error of autoregressive models in time based on the modeling of time series of TLE elements according to the principle of "moving interval".

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Application of Computational Intelligence in Order to Develop Hybrid Orbit Propagation Methods

Cited by 13 publications

References 26 publications

Hybrid SGP4 orbit propagator

Hybrid SGP4 orbit propagator

Hybrid perturbation methods based on statistical time series models

Autoregression Models of Space Objects Movement Represented by Tle Elements

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