JOURNAL ARTICLE

Toolkit for Satellite Visualization using Auxiliary Data.

  • Published In: Grenze International Journal of Engineering & Technology (GIJET), 2024, v. 10, n. 2,Part 5. P. 5988 1 of 3

  • Database: Applied Science & Technology Source Ultimate 2 of 3

  • Authored By: Veena O. S.; Suhas G. K. 3 of 3

Abstract

This paper provides a complete model for satellite tracking that uses Two Line Elements (TLE) data to accurately predict and track satellite locations over time. The three-phase graphical user interface of the model allows for real-time viewing of satellite positions and critical tracking data: input, prediction, and output. This work offers a comprehensive method for tracking and visualizing satellites in low Earth orbit (LEO), geostationary orbit, and remote sensing orbit utilizing TLE data. Precise latitude and longitude calculations allow for ongoing trajectory mapping, which makes it easier to track satellite movement dynamically. Accuracy and clarity are further enhanced by visualizing satellite swaths. Along their courses, satellite names are added to make identification easier. This integrated method has significant benefits for applications including space exploration, telecommunication, and environmental monitoring. Moreover, it makes constellation dynamics and satellite interactions more understandable, developing satellite-based technologies for a variety of useful uses. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Grenze International Journal of Engineering & Technology (GIJET). 2024/06, Vol. 10, Issue 2,Part 5, p5988
  • Document Type:Article
  • Subject Area:History
  • Publication Date:2024
  • ISSN:23955287
  • Accession Number:181734017
  • Copyright Statement:Copyright of Grenze International Journal of Engineering & Technology (GIJET) is the property of GRENZE Scientific Society and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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