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研究生: 徐子薇
Hsu, Tzu-Wei
論文名稱: 基於實際衛星操作資料之差分阻力相對軌道演化分析
Analysis of Relative Orbit Evolution Under Differential Drag Using Real Satellite Operation Data
指導教授: 莊智清
Juang, Jyh-Ching
學位類別: 碩士
Master
系所名稱: 工學院 - 太空系統工程研究所
Institute of Space Systems Engineering
論文出版年: 2026
畢業學年度: 114
語文別: 英文
論文頁數: 97
中文關鍵詞: 衛星編隊飛行 、兩行軌道跟數 、相對軌道元素 、差分阻力 、衛星操作
外文關鍵詞: Satellite Formation Flight, Two-Line Element, Relative Orbital Elements, Differential Drag
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  • 大氣阻力會顯著影響低地球軌道衛星,造成軌道衰減與軌道參數變化。小型衛星常依賴兩行軌道根數(TLE)進行事後軌道分析。然而,IRIS-F2/F3 的相對運動無法僅以單一固定漂移率假設充分解釋。因此,本研究探討 TLE 資料是否能與實際操作紀錄相互對應,並辨識在哪些條件下,觀測到的漂移變化可作為差分阻力效應的替代指標。

    本研究提出一套依據不同狀態進行分段的分析流程。首先,將 TLE、衛星健康資訊與太空天氣資料進行標準化、品質篩選,並依時間順序整合成完整資料序列。接著,依據操作狀態將資料分類為高阻力與低阻力區段。透過軌道傳播模型與 TLE 分析,萃取軌道衰減指標、半長軸變化、平均運動變化以及漂移相關指標。這些指標並非直接的物理量測結果,而是用來反映有效阻力變化的替代指標,並可作為後續軌道預測與比較分析的依據。

    本研究的主要創新在於建立一套結合歷史 TLE 資料與實際操作紀錄的姿態狀態分段分析架構,用以探討不同姿態狀態下的阻力影響。研究結果顯示,在遙測資料有限的情況下,姿態狀態分段能有效從歷史 TLE 資料中萃取出具有意義的阻力相關行為。相較於未分段的整體分析,此方法也能更清楚呈現名義狀態與高阻力狀態之間的差異。

    Atmospheric drag causes orbital decay and parameter variation in Low Earth Orbit satellites. Small satellites often rely on Two-Line Element (TLE) data for post-orbit analysis, though relative motion between satellites cannot be captured by a single constant drift assumption. This study examines whether TLE-derived behavior matches operational records and identifies when observed drift can serve as a differential-drag proxy.

    The thesis proposes a workflow that divides the data into state-dependent segments. TLE, Beacon, and space-weather information are normalized, quality-controlled, chronologically arranged, and classified into high- and low-drag segments. SGP4 and TLE analysis yield orbit-decay indicators, semi-major axis, mean motion, and drift proxies. These indicators are treated as effective drag proxies, rather than direct physical measurements, to support orbit prediction and comparison.

    The main contribution is an attitude-state segmented framework that integrates historical TLE data with operational records of IRIS-F2 and IRIS-F3 cubesates to investigate drag effects. The results show that attitude-state segmentation effectively extracts significant drag-related behavior from historical TLE data, especially with limited telemetry. It also highlights distinct differences between nominal and high-drag states compared to non-segmented analysis.

    摘要 i Abstract ii Acknowledgements iii Table of Contents iv List of Tables vi List of Figures vii List of Abbreviations viii List of Symbols x Chapter1. Introduction 1 1.1. Background 1 1.2. Research Motivation 2 1.3. Scope 4 1.4. Literature Review 4 1.4.1. Differential Drag and Relative Orbit Evolution 4 1.4.2. TLE/SGP4 Orbit Prediction and Limitations 6 1.4.3. Effect Drag and Ballistic Coefficient Estimation 7 1.5. Research Gap 8 1.6. Thesis Contributions 9 Chapter2. Data Description and Preprocessing 11 2.1. Target Satellite Pair Description 11 2.2. Historical TLE Data 13 2.3. Beacon Data 15 2.4. Automatic Event Detection 16 2.5. Space Weather Condition 18 2.6. Data Quality and Environmental Screening 21 2.7. Atmospheric Density Models 21 Chapter3. Framework 24 3.1. Overview of the Proposed Framework 24 3.2. Theoretical Background and Coordinate Systems 24 3.3. TLE-Derived Single-Satellite Indicators 27 3.4. Orbit Decay Rate Estimation 29 3.5. Effective Drag Proxy Formulation 31 3.6. Full-Period Weather and Hold-Day Diagnostics 36 3.7. Event Before-and-After Analysis 37 3.8. Relative Orbit Indicators 39 Chapter4. Data Processing Results and Discussions 44 4.1. Full Period Orbit Analysis 45 4.1.1. Full Period Space-Weather and Hold-Day Context 47 4.1.2. Full Period Drag State Calibration 52 4.1.3. Ideal Drag State Coefficient Reference 53 4.2. Overview of the Current Switch Cases 55 4.3. Single-Satellite Semi-Major-Axis Response 57 4.4. Relative Along-Track and Phase Response 59 4.5. QROE Relative Orbit Results 61 4.6. Indicator Distributions and Drag-Proxy Context 64 4.7. Space-Weather and Sampling Limitations 65 4.8. Case Interpretation 68 4.9. Summary 69 Chapter5. Prediction and Validation of Relative Orbit Evolution 70 5.1. Prediction Problem Definition 70 5.2. STK/MATLAB Prediction Configuration 71 5.3. Prediction Metrics 72 5.4. Prediction Results 73 5.5. Discussion 76 5.6. Summary 77 Chapter6. Conclusion and Future Work 78 6.1. Conclusions 78 6.2. Future Work 79 References 81

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