[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126119-en":3,"doc-seo-126119-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126119,5909887254083,"Miles","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine Learning for LEO and MEO Satellite Orbit Prediction - Proceedings paper","Accurate orbit prediction supports space geodesy applications such as space situational awareness, orbital maneuvers, and real-time satellite navigation. Conventional analytical and numerical propagation methods rely on limited dynamic models and can struggle to represent complex satellite motion. This study evaluates multiple machine-learning and deep-learning approaches for low Earth orbit (LEO) orbit prediction, then extends the analysis to medium Earth orbit (MEO). Preprocessed LEO Swarm-A precise orbit products and MEO GNSS final ephemeris products train models that estimate position and velocity. Model results for LEO and MEO are compared and discussed, showing strong potential for improving accuracy and reliability.","This is a self-archived – parallel published version of this article in the publication archive of the University of Vaasa. It might differ from the original.  \nMachine Learning for LEO and MEO Satellite Orbit Prediction  \nAuthor(s): Selvan, Kannan; Siemuri, Akpojoto; Prol, Fabricio S.; Välisuo, Petri; Kuusniemi, Heidi  \nTitle: Machine Learning for LEO and MEO Satellite Orbit Prediction  \nYear: 2024  \nVersion: Publisher’s PDF  \nCopyright ©2024 Author(s). Published by Institute of Navigation.  \nPlease cite the original version:  \nSelvan, K., Siemuri, A., Prol, F, S., Välisuo, P., & Kuusniemi, H. (2024) . Machine Learning for LEO and MEO Satellite Orbit Prediction.  \nProceedings of the 37th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GNSS+ 2024),(pp. 3556-3571) . Institute of Navigation.  \n[https://doi.org/10.33012/2024.19765](https://doi.org/10.33012/2024.19765)  \nMachine Learning for LEO and MEO Satellite  \nOrbit Prediction  \nKannan Selvan 1 , Akpojoto Siemuri 1 , Fabricio S. Prol 1,2 , Petri Välisuo 1 , Heidi Kuusniemi 1,2  \n1 School of Technology and Innovations, University of Vaasa, Finland  \n2 Finnish Geospatial Research Institute, National Land Survey, Finland  \nBIOGRAPHY  \nKannan Selvan received his B.Sc.(tech) degree in Electronics and Communication Engineering from Anna University, India in 2012, the M.Sc.(tech) degree in Communications and Systems Engineering from the University of Vaasa, Finland in 2020 . He is currently pursuing a Ph.D. degree in automation technology at the University of Vaasa. From 2018 to 2020, he was a Research Assistant in the Digital Economy Research Platform at the University of Vaasa, Finland. He is currently a Project Researcher atthe University of Vaasa. His research interest includes GNSS technologies, LEO-PNT, satellite-data analysis, machine learning, satellite communication, smart devices, and embedded Systems.  \nAkpojoto Siemuri received his B.Sc.(tech) degree in electrical and computer engineering from the Federal University of Technology Minna, Nigeria in 2010, an M.Sc.(tech) degree in wireless industrial automation with a minor study in industrial management from the University of Vaasa, Finland in 2019 . He is currently pursuing a Ph.D. degree in automation technology atthe University of Vaasa. From 2018 to 2019, he was a Research Assistant in the Smart Energy Systems Research Platform (SESP) Project at the University of Vaasa, Finland. He is currently a Project Researcher at the University of Vaasa. His research interest includes machine learning, GNSS technologies, smart devices, embedded systems, communication systems, and game theory.  \nFabricio dos Santos Prol is a Senior Researcher at the Finnish Geospatial Research Institute (FGI) in the National Land Survey of Finland (NLS) -Finland. He is also a Docent Fellow in the University of Vaasa. The focus of his current research lies in GNSS positioning, LEO-PNT systems, ionospheric modeling, and data assimilation.  \nPetri Välisuo is currently working as an Associate Professor (tenure track), in sustainable automation, at the School of Technology and Innovations, University of Vaasa, Finland. He received M.Sc.(tech) degree in computer science from the Tampere University of Technology, Finland, and a D.Sc.(Tech) degree in automation technology from the University of Vaasa, in years 1996 and 2011 respectively. He has authored and co-authored 27 peer-reviewed and more than 10 other scientific publications. His research interests cover machine learning, IoT, positioning methods, and other technologies relevant to industrial automation. He has been working for 10 years in the telecommunication industry before his research career at the University of Vaasa.  \nHeidi Kuusniemi is a professor of computer science and director of Digital Economy at the University of Vaasa in Finland. She is also a part-time research professor in satellite navigation at the Finnish Geospatial Research Institut","cbCaiiicCdEUmgu6","https://ap.wps.com/l/cbCaiiicCdEUmgu6","pdf",1006437,6,1,17,"English","en",105,"# Abstract\n## Problem and motivation\n## Data sources and preprocessing\n## ML model training and estimation\n## Evaluation for LEO and MEO\n## Comparison and conclusions","[{\"question\":\"Why is accurate satellite orbit prediction important in this work?\",\"answer\":\"It enables space geodesy tasks such as space situational awareness, orbital maneuvers, and real-time satellite navigation.\"},{\"question\":\"What limitations are noted for traditional orbit prediction methods?\",\"answer\":\"They depend on limited dynamic models, which may not capture the complex dynamics of satellite motion accurately.\"},{\"question\":\"Which satellites and data products are used to train and evaluate the ML models?\",\"answer\":\"LEO Swarm-A precise orbit products and MEO GNSS final ephemeris products are preprocessed for training various ML models.\"}]","Machine Learning for LEO and MEO Satellite Orbit Prediction - 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