本研究將探討如何使用遞歸式最小平方近似法 (Recursive Least Square Method)求解慣性模組的校正矩陣，及如何適當選用水下載具之感測器後利用強健型卡爾曼濾波器(Robust Kalman Filter)實現雜訊抑制，並確保估測器能在無法使用GPS進行校正的條件下，準確估測無平移水下載具之姿態及位置。此慣性定位之演算法流程大致為1.針對系統進行定義，並建構出此系統之差分方程式，2.再藉由自體-大地座標轉換理論(Principle Rotation)將慣性模組之感測值導入此差分方程，3.最後應用重力加速度分量、陀螺儀指北技術(North Finding Technique)、船速計與強健型卡爾曼濾波器針對此系統進行濾波進而得出準確的估測值，與4.最後再將此資訊界座標轉換理論轉換至大地座標以達成慣性定位之實作。

The main contribution of this thesis is to derive the complete compensation matrix of all used inertial sensors by using Recursive Least Square Method and select suitable sensors for underwater vehicles. Furthermore, elimination of noises of these sensors can be done via a Robust Kalman Filter. Thus, attitude and position of the proposed inertial sensing system can be estimated precisely, even an underwater vehicle is sheltered and cannot be calibrated by GPS signal. The estimated procedure of inertial alignment algorithm can be briefly expressed as follows: 1. Describe the mathematical expression for the inertial measurement unit, 2. Import sensor signals into the this model by using Principle Rotation criterion, 3. Purify the noised inertial sensing signals with adopting sensors and methodologies including, accelerator for measuring gravity, ship speedometer, the northing finding technique, and a robust Kalman Filter, and 4. Transfer the estimation results into earth-frame for navigation of underwater vehicles.

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