本研究利用飛航資料紀錄器所記錄之真實飛航資料進行性能分析。先進行初步估測，之後利用擴展式卡爾曼濾波器（Extended Kalman Filter，EKF）進行估測，依據力平衡與力矩平衡方程式求出其沿飛航軌跡之升力係數變化，再帶入輻狀基底函數類神經網路（Radial Basis Function Neural Network，RBFNN），以不同升力模型與資料分群法，估算出不同的升力導數，將所得結果進行比較。結果顯示擴展式卡爾曼濾波器可以改善飛行參數的誤差，而輻狀基底函數類神經網路的結果顯示穩態升力模型可能無法完整表達高動態下的升力模式。

This study provides an approach to estimate flight performance by real flight data records from flight data recorder. The priori flight states are introduced. The approach use Extended Kalman Filter to reduce noise and bias of flight data records. Use dynamic equations to estimate lift coefficient at every single time in flight. Use different clustering method and different lift coefficient model on Radial Basis Function Neural Network to estimate lift derivatives and make comparisons. The study shows that Extended Kalman Filter can enhance the flight states, and Radial Basis Function Neural Network shows the steady lift model can not describe lift pattern in high-dynamic flight properly.

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