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系統識別號 U0026-0408201411475300
論文名稱(中文) 利用都卜勒頻移估算衛星TLE軌道參數
論文名稱(英文) Estimation of TLE Orbital Parameters of Satellites using Doppler Shift
校院名稱 成功大學
系所名稱(中) 電機工程學系
系所名稱(英) Department of Electrical Engineering
學年度 102
學期 2
出版年 103
研究生(中文) 沈廣程
研究生(英文) Jesus Sanchez
電子信箱 jesussanchez85@gmail.com
學號 N26017027
學位類別 碩士
語文別 英文
論文頁數 91頁
口試委員 指導教授-莊智清
口試委員-莊哲男
口試委員-苗君易
口試委員-林穎裕
口試委員-壽鶴年
中文關鍵字 衛星軌道參數  都卜勒頻移  軟體定義無線電  遺傳算法 
英文關鍵字 TLE  Doppler shift  SDR  Genetic Algorithm 
學科別分類
中文摘要 衛星軌道參數(TLE)常用於計算特定時間的衛星位置,其中軌道參數係以地球為中心的慣性系統做為參考。這些軌道參數以公開的形式由北美防空司令部(NORAD)給定,近年來已成為衛星追蹤的的參考基準。當使用SGP4模型以及衛星軌道參數,對於衛星位置和速度的預測,具有一定程度的準確性;然而,衛星軌道參數的準確性會受到軌道預測的時間變化而降低。因此,為了維持軌道參數的準確性,必須週期性地予以更新。對於低地軌道衛星,特別是針對微型衛星(CubeSats),這些參數的更新和衛星的辨識在地面站的操作中相當重要,由於它們的軌道平均可用的通訊時間通常限制在平均每天以分為單位,所以相當程度的準確性對於衛星追蹤來說是必須的。
  本篇研究針對近地軌道衛星,根據兩種輸入值決定其較佳的軌道參數,其中一種是衛星軌道參數,另一種則是最新的追蹤數據;本篇研究提出使用最佳化過程來計算參數的改善,這個過程涵蓋分析兩個以遺傳演算法設計的模型,其中這兩個模型受到都卜勒效應影響,一個模型是基於頻率的變化,使用先前的軌道參數作為起始值,另一個模型是從特定位置接收到的實際頻率變化。在第一個方法當中,SGP4模型的使用是為了在給定的座標系統中,計算不同期間的位置和速度。而第二個方法中,中心頻率可直接從傳輸時間量測。由於噪音、衰減以及當地干擾的影響,有個演算法被使用來追蹤中心頻率。在同時使用預測值與量測值的差值,其位置與速度的較佳近似值可通過使用這個差值作為遺傳演算法合適的參數;最好的基因可降低都卜勒效應的量測誤差,來生成一組新的軌道參數(TLE)。
英文摘要 The Two-Line-Elements (TLE) of satellites contains the orbital parameters used to calculate its position at a determined time based on a referential Earth Centered Inertial (ECI) system. These parameters are given publicly by the North American Aerospace Defense Command (NORAD) and used nowadays as a conveyed reference for satellite tracking. By using the SGP4 algorithm and the TLE parameters, the position and velocity of the satellite can be predicted with an acceptable level of accuracy. However, the accuracy of the TLE parameters is subject to degradation when the time of orbit prediction deviates from reference epoch. Thus, the TLE parameters need to be updated periodically in order to maintain the quality in orbit predication. For Low-Earth Orbiting (LEO) satellites, specifically targeted for Cubesats whichnowadays are used for research, the update of these parameters and identification of the satellites is critical for the operation in the ground station. Because the nature of its orbits the average available communication time with them is limited to some minutes in a day, thus certain degree of accuracy is needed for its tracking.

The research focuses on the determination of a better approximation of the orbital parameters of LEO satellites based on two inputs: one is the TLE parameters that might be outdated and the other is a recent tracking data of the LEO satellite. In this research, an optimization process that improves the parameters for tracking purposes is investigated. This process involves the analysis of the transmission affected by the Doppler Effect in two models that are included in a genetic algorithm design. One model is based on the frequency variation using the previous orbital parameters as starting approximation, and the other is the actual frequency variation received from a specific location. In the first approach, the SGP4 model is used in order to make the calculation of the position and velocity at different periods of time in a given coordinate system. In the second approach the center frequency over time of the transmission is obtained directly from measurements. Due to noise, attenuation and local interference, an algorithm is used to track the center frequency. Using the difference of the prediction and the real value measured at the same time, a better approximation of the position and velocity can be made by using the genetic algorithm approach. The best gen that can reduce the error of the Doppler Effect measurements are used to generate a new TLE which includes better approximation of the following days.
論文目次 摘要 I
Abstract III
Acknowledgements V
Contents VI
List of Tables VIII
List of Figures X
Chapter 1. Introduction 1
1.1. Motivation 1
1.2. Organization 5
Chapter 2. Fundamentals 7
2.1. TLE parameters 7
2.2. SGP4 and coordinate systems 12
2.3. Doppler Effect 14
2.4. Genetic Algorithm 17
2.5. Software Defined Radio (SDR) 20
2.6. Network Time Protocol (NTP) 22
2.7. Digital Signal Processing (DSP) 23
2.8. Inflection point in Polynomial Fit 24
Chapter 3. Implementation 25
3.1. Stage 1: Acquisition 25
3.2. Stage 2: Digital Signal Processing 30
3.3. Stage 3: Genetic Algorithm 34
3.4. Stage 4: Verification 38
Chapter 4. Results and Discussion 40
4.1. Standalone simulations 40
4.2. Processing of Real Data 55
4.2.1. CUTE 1.7+APD II 55
4.2.2. INVADER 69
4.3. Discussion 82
Chapter 5. Conclusions 83
5.1. Conclusions 83
5.2. Limitations 83
5.3. Future Research 84
Reference 86
Appendix A 89
Appendix B 90
Appendix C 91
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