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Research Article

Analysis of operational trends and essential modeling factors for enhancing the utilization of low earth orbit satellites
Du-hui Yun1
,
Seok Teak Yun1*
1Department of Aerospace and Software Engineering, Gyeongsang National University, Jinju 52828, South Korea
*Corresponding Author
31 December 2025. pp. 29-44
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Abstract

In this paper, we analyze the latest research trends to maximize the operational efficiency of low Earth orbit (LEO) satellites and identify the essential technologies required for this purpose. Traditional satellite operation methods face challenges due to the limited communication environment with ground stations, making real-time status monitoring difficult and leading to conservative mission operations. This approach not only reduces resource utilization but also increases operational costs and introduces uncertainties in mission execution. Moreover, the lack of predictive models that account for the long-term degradation and aging of satellite systems hinders the development of reliable simulators and mission optimization. Recent studies have highlighted the potential of artificial intelligence (AI) and machine learning (ML)-based predictive models to address these challenges. AI-based models enable precise state diagnostics even when satellites are in non-contact periods, outperforming traditional ground station-dependent methods. As satellite operational environments become increasingly complex and mission requirements diversify, the ability to analyze vast telemetry data in real time and detect anomalies at an early stage has become crucial. Reflecting this need, AI and ML technologies have been widely applied in various domains, including power system anomaly detection, battery performance degradation prediction, and attitude control optimization, demonstrating their effectiveness. This paper examines how AI-based technologies can enhance the efficiency of LEO satellite operations and presents key modeling techniques for effectively implementing such predictive models. Specifically, we discuss the necessity of modeling power conversion systems, energy storage devices, and attitude prediction systems while evaluating the contributions of AI-based predictive models to improving satellite operational performance.

Keywords
LEO satellites
Artificial intelligence (AI)
Machine learning (ML)
System state prediction
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Information
  • Publisher :Korean Academy of Space Security
  • Publisher(Ko) :한국우주안보학회
  • Journal Title :JOURNAL OF SPACE SECURITY
  • Journal Title(Ko) :한국우주안보학회지
  • Volume : 2
  • No :2
  • Pages :29-44
  • Received Date : 2025-03-04
  • Revised Date : 2025-06-23
  • Accepted Date : 2025-11-21
  • DOI :https://doi.org/10.23386/joss.2025.2.2.004
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