Early cataloguing of fragments from break-up events

Pastor, Alejandro Pérez; Siminski, Jan; Escribano, Guillermo; Sanjurjo-Rivo, Manuel; Escobar, Diego A.

doi:10.1016/j.asr.2023.02.016

Cited by 2 publications

(3 citation statements)

References 22 publications

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“…Space object observations processing requires the identification of an object by its heterogeneous observations while matching them with the catalogued ones. Nowadays the identification problem is usually solved by calculating correlations between simulated orbits of catalogued objects and the observed trajectory [7,8]. The method for preliminary RSO classification by its type or satellite constellation, proposed in this article, narrows the number of potential object matches several times, which can increase the speed of the whole object identification process significantly.…”

Section: Intoductionmentioning

confidence: 99%

Resident space objects classification by machine learning techniques

Samorodova

2024

J. Phys.: Conf. Ser.

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In recent years the number of resident space objects in near-Earth space has been increasing drastically, which can lead to a “cascade collisions” effect and other catastrophic consequences. Therefore, it is crucial to monitor resident space objects, maintain and update catalogues on time, including the identification of unknown space objects to estimate their location and potential orbit. It is necessary to perform resident space object identification quickly and accurately to avoid possible satellite collisions resulting in large clouds of uncontrollable space debris fragments. That is why the task of resident space object preliminary classification by TLEs is stated and solved by applying machine learning approaches to achieve a higher classification quality and speed. This issue is a highly imbalanced classification problem, which narrows and specifies the used models. Applying the proposed resident space object classification model to space surveillance systems can decrease the time required for the object identification significantly.

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Section: Intoductionmentioning

confidence: 99%

Resident space objects classification by machine learning techniques

Samorodova

2024

J. Phys.: Conf. Ser.

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“…Lei et al proposed a geometric method for data association in space object cataloguing and conducted research on the space object catalogue system based on optical observation [14]. Pastor et al described the process of establishing, maintaining, applying the space object catalogue, and compiled existing methods used in cataloguing, including IOD, data association, orbit determination, and manoeuvre detection [15]. Reihs et al and Liu et al accounted for the perturbation of Earth's oblateness in the association of radar tracklets [16,17].…”

Section: Introductionmentioning

confidence: 99%

“…The development of a space object catalogue involves two main tasks: maintaining the orbits of existing objects and discovering new objects [15,22]. Catalogue maintenance involves updating the orbits of known space objects using newly acquired observations from the space surveillance network.…”

Section: Introductionmentioning

confidence: 99%

New Space Object Cataloguing through Very-Short-Arc Data Mining

Liu,

Li,

Sang

et al. 2023

Remote Sensing

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The space surveillance network collects significant quantities of space object monitoring data on a daily basis, which varies in duration and contain observation errors. Cataloguing space objects based on these data may result in a large number of very short arcs (VSAs) being wasted due to cataloguing flaws, poor data quality, data precessing, and so on. To address this problem, an effective data mining method based on tracklet-to-object matching is proposed to improve the data utilization in new object cataloguing. The method can enhance orbital constraints based on useful track information in mined tracklets, improve the accuracy of catalogued orbits, and achieve the transformation of omitted observations into “treasures”. The performance of VSAs is evaluated in tracklet-to-object matching, which is less sensitive to tracklet duration and separation time than initial orbit determination (IOD) and track association. Further, the data mining method is applied to new space object cataloguing based on radar tracklets and achieved significant improvements. The 5-day data utilization increased by 9.5%, and the orbit determination and prediction accuracy increased by 11.1% and 23.6%, respectively, validating the effectiveness of our method in improving the accuracy of space object orbit cataloguing. The method shows promising potential for the space object cataloguing and relevant applications.

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Early cataloguing of fragments from break-up events

Cited by 2 publications

References 22 publications

Resident space objects classification by machine learning techniques

Resident space objects classification by machine learning techniques

New Space Object Cataloguing through Very-Short-Arc Data Mining

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