摘要 卡爾曼濾波器是一種遞迴式的最佳化估算，廣泛應用於整合式導航。卡爾曼濾波器需要確切的知道動態模型及雜訊統計，而且雜訊統計必須為零均值白雜訊，若濾波器的系統建模和真實情況不符合，即有造成相當大之誤差，甚至引起濾波發散之可能。自適應濾波是一種具有抑制濾波器發散的濾波方式，在本論文中，提出一種新的自適應濾波方法應用於整合式導航系統。吾人利用殘差序列的資訊來估測當前時刻的估測誤差斜方差矩陣及觀測量雜訊協方差矩陣設計一新穎之濾波器，且應用於整合式導航系統之設計，並與傳統卡爾曼濾波器之性能進行比較。 Abstract The well-known Kalman filtering is a form of optimal estimation characterized by recursive evaluation, which has been widely applied to the integrated navigation designs. Kalman filter requires that all the plant dynamics and noise processes are exactly known, and the noise process is zero mean white noise. If the theoretical behavior of a filter and its actual behavior do not agree, divergence problems will occur. The adaptive algorithm has been one of the approaches to prevent divergence problem of the Kalman filter when precise knowledge on the system models are not available. A novel adaptive Kalman filtering approach with application to the integrated navigation system is presented. In the proposed approach, adaptation of the estimation covariance matrix and measurement noise covariance matrix is conducted, by monitoring the parameters based on the innovation information. Performance improvement using the proposed method will be evaluated and compared with that by conventional Kalman filter approach.