本研究採取依飛行器在空間的位置，透過微風暴（Microburst）風切流場的數學模型，取得當下的風場強度與方向以實現飛行狀態模擬，進而對飛行數據進行參數識別與狀態估測。在本研究中參考兩步驟法（Two-step Method）的概念，步驟一先對無風狀態下的機載數據進行相容性測試，估測出空速及攻角的偏差常數（Constant Bias Error）及偏差比例因子（Scale Factor Error），步驟二再進行風切流場分析以估測出風場，並進而估測出其他參數的偏差常數。本文採用兩步驟法並配合使用基因演算法（Genetic Algorithms）作為估測系統誤差參數的優化方法，並以擴展型卡氏濾波器（Extended Kalman Filter）估測系統狀態變數，以此來對亂流中的飛行數據進行參數識別與狀態估測，進而重建飛行軌跡及風切流場。本研究採用由最大可能性原理（Maximum Likelihood Principle）所推導的目標函數（Objective Function）作為優化目標，經由優化此目標函數以獲得系統誤差參數的最佳估測。在估算出系統誤差參數後，修正系統狀態方程式，將估算之狀態變數經時間積分重建飛行軌跡。本研究可看出基因演算法結合兩步驟法能夠成功的估測出的偏差常數及偏差比例因子，並重建出在遭遇風切流場中的飛行軌跡與所經歷的風場分佈。

　　The objective of this research is to reconstruct the wind field and the flight path for aircraft in wind shear flow encounter. Wind field experienced by the flying aircraft is generated by a microburst mathematical model and the aircraft position. Two-step method is introduced, in which the Genetic Algorithm is used in conjunction with the Extended Kalman Filter to identify the system error parameters and to estimate the state variables. The first-step identification uses flight data obtained in smooth air, whereas the second-step assumes the error factors associated with the air data are invariant when analyzing the flight data in turbulence. The objective function is derived using the Maximum Likelihood principle, and the system error parameters are obtained by optimizing this objective function. The use of Genetic Algorithm aims to solve the numerical stiffness and the initial guess problems associated with the gradient search methods. In this research a Boeing 747-100 airplane encountered with 3 different wind shear fields during approaching are simulated. The results of constant bias and scale factors estimated by the present Genetic Algorithm were satisfactory. The state variables and flight paths and the wind shear fields are successfully reconstructed, indicating the appropriateness of applying the present method to wind shear flight data analysis.

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