Hybrid SGP4 orbit propagator

San-Juan, Juan F.; Pérez, Iván; San-Martín, Montserrat; Vergara, Eliseo P.

doi:10.1016/j.actaastro.2017.04.015

Cited by 30 publications

(6 citation statements)

References 19 publications

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“…This work was closely followed by [55], in which the same method was applied, alongside a bias correction function. Similarly, other authors [56,57] have shown improvement of the SGP4 prediction using error correction functions, based on prediction error periodicity.…”

Section: Orbit Predictionmentioning

confidence: 67%

Machine Learning in Orbit Estimation: a Survey

Caldas¹,

Soares²

2022

Preprint

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Since the late '50s, when the first artificial satellite was launched, the number of resident space objects (RSOs) has steadily increased. It is estimated that around 1 Million objects larger than 1 cm are currently orbiting the Earth, with only 30,000, larger than 10 cm, presently being tracked. To avert a chain reaction of collisions, termed Kessler Syndrome [1], it is indispensable to accurately track and predict space debris and satellites' orbit alike. Current physics-based methods have errors in the order of kilometres for 7 days predictions, which is insufficient when considering space debris that have mostly less than 1 meter. Typically, this failure is due to uncertainty around the state of the space object at the beginning of the trajectory, forecasting errors in environmental conditions such as atmospheric drag, as well as specific unknown characteristics such as mass or geometry of the RSO. Leveraging datadriven techniques, namely machine learning, the orbit prediction accuracy can be enhanced: by deriving unmeasured objects' characteristics, improving non-conservative forces' effects, and by the superior abstraction capacity that Deep Learning models have of modelling highly complex non-linear systems. In this survey, we provide an overview of the current work being done in this field.

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Section: Orbit Predictionmentioning

confidence: 67%

Machine Learning in Orbit Estimation: a Survey

Caldas¹,

Soares²

2022

Preprint

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“…However, it is different from this study as it shows the opposite way. For example, a study by [9] shows that using more input features gave higher accuracy. However, they are using the Holt Winter, a tricky statistical technique that requires more input data.…”

Section: Resultsmentioning

confidence: 99%

Modeling Orbital Propagation Using Regression Technique and Artificial Neural Network

Salleh¹,

Yuhaniz²,

Azmi³

2022

International Journal on Advanced Science, Engineering and Information Technology

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Orbital propagation models are used to predict the position and velocity of natural and artificial objects orbiting the Earth. It is crucial to get accurate predictions to ensure proper satellite operational planning and early detection of possible disasters. It became critical as the number of space objects grew due to many countries scrambling to explore space for various purposes such as communications, remote sensing, scientific mission, and many more. Physical-based and mathematical expression approaches provide orbital propagation with high accuracy. However, these approaches require substantial expenditure to provide suitable facilities and are complicated for those with no expertise in this field. The orbital propagation model is developed using regression techniques and artificial neural networks in this study. The aim is to have a reliable and precise orbital propagation model with minimal computational and cost savings. The past orbital data is used instead of complicated numerical equations and expensive tools. As a result, the trained orbital propagation model with accuracy up to 99.49% with a distance error of 18.73km per minute is achievable. The trained model can be improved further by modifying the network model and various input data. This model is also expected to provide vital information for organizations and anyone interested. Finally, this research can help organizations with insufficient resources to have their orbit propagation model without special tools or rely on other countries with satellite data at a lower cost.

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“…The SGP models use average orbital elements, whose values are obtained using a special procedure for removing short-period variations [10]. SGP models have been developed over decades [17] and are now widely used [7,15].…”

Section: Introductionmentioning

confidence: 99%

Analytical model of satellite motion in almost circular orbits under the influence of zonal harmonics of geopotential

Pirozhenko¹,

Maslova²,

Vasyliev³

2022

Kosm. nauka tehnol.

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The article deals with the movement of satellites in low near-circular orbits of the Earth. An analytical model is constructed, which consists of formulas describing the change of the osculating elements and averaged equations. An algorithm for constructing a second approximation of the influence of zonal harmonics of the geopotential on the movement of satellites in almost circular orbits is presented. For the second and third zonal harmonics, formulas are given for the osculating and average elements describing the motion of the satellite in the second approximation in small parameters. The introduction of special variables for almost circular orbits made it possible to significantly simplify the procedure for constructing the second approximation of the influence of zonal harmonics. The article provides a justification for the accuracy of the analytical model for the considered orbits. The constructed model of changes in the average elements of the orbit describes the basic principles of motion. With a sufficiently high accuracy, this model describes the changes in the average elements of the orbit with simple analytical formulas and is convenient for analyzing the properties of orbits and pre-selecting a reference orbit for a specific mission.

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Hybrid SGP4 orbit propagator

Cited by 30 publications

References 19 publications

Machine Learning in Orbit Estimation: a Survey

Machine Learning in Orbit Estimation: a Survey

Modeling Orbital Propagation Using Regression Technique and Artificial Neural Network

Analytical model of satellite motion in almost circular orbits under the influence of zonal harmonics of geopotential

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