Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4020
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dc.contributor.authorSaxena, Akshita-
dc.contributor.authorBaraha, Satyakam-
dc.contributor.authorSahoo, Ajit Kumar-
dc.date.accessioned2023-06-07T06:44:20Z-
dc.date.available2023-06-07T06:44:20Z-
dc.date.issued2023-05-
dc.identifier.citationInternational Conference on Microwave, Optical and Communication Engineering, IIT Bhubaneswar, India, 26 - 28 May 2023en_US
dc.identifier.urihttp://hdl.handle.net/2080/4020-
dc.descriptionCopyright belongs to proceeding publisheren_US
dc.description.abstractOrbit prediction is crucial and important for satellite tracking. To improve prediction, a person must be well equipped with knowledge of the earth’s gravitational pull, atmospheric drag, radiation pressures, basic manoeuvring objects, and other information. Thus, orbit prediction has advanced in physics-based models. Most of the time, the above-mentioned information is not publicly available. Data related to satellites is kept with the respective space organizations. Using this concept, the proposed approach employs gradient boost regression trees (GBRT) with two-line element (TLE) data and, when compared to recently developed machine learning techniques such as artificial neural networks (ANN), support vector machines (SVM), and Gaussian processes (GP), it provides improved orbit prediction accuracies in terms of position and velocity. Further, the proposed method avoids the overfitting issue and shows better approximation ability. The simulations are carried out for a total of six resident space objects in low earth orbit, medium earth orbit, and sun synchronous orbit.en_US
dc.subjectTwo line elementen_US
dc.subjectGBRTen_US
dc.subjectmachine learningen_US
dc.subjectorbit predictionen_US
dc.titleImproved Orbit Prediction using Gradient Boost Regression Treeen_US
dc.typeArticleen_US
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