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系統識別號 U0026-2907201612000000
論文名稱(中文) 利用ADS-B 1090MHz機載資料發展航空氣象資訊及異常氣象偵測系統雛型之研究
論文名稱(英文) Development of an aviation meteorological information and unusual weather detection system prototype based on the ADS-B 1090MHz data
校院名稱 成功大學
系所名稱(中) 民航研究所
系所名稱(英) Institute of Civil Aviation
學年度 104
學期 2
出版年 105
研究生(中文) 彭晧瑋
研究生(英文) Hao-Wei Peng
學號 Q46031027
學位類別 碩士
語文別 英文
論文頁數 72頁
口試委員 指導教授-詹劭勳
口試委員-王大中
口試委員-袁曉峰
中文關鍵字 廣播式自動回報監視  全球預報系統  偵測氣象異常演算法  支持向量機 
英文關鍵字 Automatic Dependent Surveillance-Broadcast (ADS-B)  Global Forecast System (GFS)  unusual aviation weather detection algorithm  Support Vector Machine (SVM) 
學科別分類
中文摘要 航空氣象一直以來與飛航安全有密不可分的關係,許多飛航事故都肇因於航空氣象之異常狀況,例如:低空風切(Low-level wind shear)及微爆氣流(Microburst)。為了達到預警的效果,現行的航空異常氣象偵測系統為低空風切警報系統(Low-Level Wind Shear Alert System, LLWAS),然而此系統架設的氣象觀測站皆分佈於鄰近地表之位置,其偵測到的氣象異常狀況缺乏高程氣象資訊。因此,本論文將利用廣播式自動回報監視(Automatic Dependent Surveillance-Broadcast, ADS-B)系統所提供的高程氣象資訊來增強現行的氣象偵測系統。另一方面,本論文也利用航機的ADS-B系統驗證氣象異常狀況與航機資料之間的關聯性並建立異常氣象偵測系統之雛型。
  為了取得高程氣象資訊以增強現有氣象偵測系統,本論文首先對航機廣播之ADS-B Mode-S ES氣象參數進行分析。由於ADS-B Mode-S ES氣象參數中磁向角及真空速資料需要經過修正才可以進一步計算出正確的高程風速向量資訊,本論文藉由全球預報系統(Global Forecast System, GFS)與航機的氣象資料設立修正資料庫,並依據不同的航機機型做參數之修正。接著,為了驗證航機氣象資料與氣象異常狀況之關聯性,本論文將利用三種氣象異常演算法來呈現在航機遭遇異常氣象之狀況,三種氣象異常演算法分別是氣象參數跳動演算法、風速向量差演算法及觀測航機異常位移之方法。最後,本論文將利用機器學習之支持向量機(Support Vector Machine, SVM)演算法來設立異常氣象狀況的資料庫,經由上述之氣象異常演算法及特徵選擇技術來挑選機器學習之特徵向量,藉此建立異常氣象偵測系統之雛型以判斷氣象異常之發生。
在本篇論文中,我們利用航機廣播的ADS-B Mode-S ES資料提供高程的氣象資訊,並且利用GFS建立航機參數的修正資料庫以獲得正確的高程風速向量資訊。並且本論文藉由三種不同氣象異常演算法及機器學習之SVM演算法建立出異常氣象偵測系統之雛型。
英文摘要 Aviation meteorology is closely associated with flight safety, as many accidents have occurred due to unusual weather conditions. However, the current aviation unusual weather detection systems, such as the low level wind shear alert system, can only detect unusual weather conditions near the ground surface, because all ground-based meteorological monitoring stations are at almost the same level. It is thus necessary to augment the current system, and by doing so all aircraft with ADS-B systems can serve as weather sensors in the air to collect vertical weather information. As a result, the objective of this research is deriving the vertical wind profile from ADS-B Mode-S ES data, evaluating the correlation between the decoded ADS-B Mode-S ES data and the unusual weather conditions, and building an unusual weather detection system prototype.

In order to acquire the vertical wind profile, this thesis analyzes the weather parameters decoded from the ADS-B Mode-S ES data. Because the weather parameters of magnetic heading and true airspeed need certain adjustments to get the accurate vertical wind profile, this thesis establishes an adjustment database for these two parameters with the assistance of the Global Forecast System (GFS). Next, this thesis discusses the feasibility of the vertical wind profile for use with three unusual weather algorithms, which are the meteorological parameter fluctuation processing algorithm, wind shear detection algorithm and abnormal movement detection algorithm. This thesis also presents the correlation between ADS-B Mode-S ES data and the unusual weather conditions. Furthermore, this thesis utilizes the above algorithms to find the features used in the machine learning algorithm, Support Vector Machine (SVM), and applies a feature selection technique to establish the learning model. This learning model can classify whether the feature sets derived from the ADS-B Mode-S ES data are in the normal condition or the unusual weather condition, and thus serves as an unusual weather detection system prototype.

The weather parameters derived from the ADS-B Mode-S ES data are adjusted in order to acquire the accurate vertical wind profiles. The three unusual weather algorithms evaluate the correlation between ADS-B Mode-S ES data and unusual weather conditions. Finally, this thesis uses the machine learning method to build an unusual weather detection system prototype to detect unusual weather conditions encountered by aircraft.
論文目次 摘要 I
Abstract III
Table of Contents V
List of Tables VIII
List of Figures IX
CHAPTER 1 INTRODUCTION AND OVERVIEW 1
1.1 Motivation and objectives 2
1.2 Automatic Dependent Surveillance - Broadcast 5
1.3 Global Forecast System 8
1.4 Machine learning method 10
1.5 Previous works 12
1.6 Thesis organization 14
CHAPTER 2 VERTICAL WIND PROFILES ACQUISITION PROCESS 15
2.1 Method of acquiring the vertical wind profiles 16
2.2 Adjustment of magnetic heading 19
2.3 Adjustment of true airspeed 22
2.4 Results of overall adjustment 25
2.4.1 Magnetic heading adjustment database 26
2.4.2 True airspeed adjustment database 26
2.4.3 Verification of the adjusted results 27
2.5 Interim summary 30
CHAPTER 3 ALGORITHMS AND MACHINE LEARNING IN UNUSUAL WEATHER CONDITIONS 31
3.1 Unusual weather algorithms 32
3.1.1 Meteorological parameter fluctuation processing algorithm 32
3.1.2 Wind shear detection algorithm 36
3.1.3 Abnormal movement detection algorithm 41
3.2 Machine learning in unusual weather conditions 45
3.2.1 Process of machine learning method 45
3.2.2 Feature selection process 48
3.3 Interim summary 50
CHAPTER 4 EXPERIMENTAL RESULTS AND ANALYSES 51
4.1 Experiment setup 52
4.2 Acquisition of vertical wind profiles 55
4.3 Machine learning model establishment and classification results 57
4.3.1 SVM learning model establishment 57
4.3.2 SVM classification results 59
4.4 Interim summary 67
CHAPTER 5 CONCLUSIONS AND FUTURE WORKS 68
5.1 Conclusions 68
5.2 Future works 70
Reference 71
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