目前全球衛星定位系統(GPS，Global Positioning System)已被廣泛運用於各個導航系統，如武器導航、船舶導航和車輛導航等，但GPS訊號容易被建築物和地形地物遮蔽使得導航困難，為了克服此情況，將慣性導航定位系統（INS，Inertial Navigation System）與GPS整合。當GPS定位系統離線時，INS可自主產生定位訊息：角速度及加速度，原則上將它對時間進行一次積分及二次積分可求得於三度空間之速度、姿態角和位置訊息，然而慣性感測元件具有訊號偏移(bias)、及雜訊等特性，經過積分運算之後，其誤差會隨時間增加而不斷累積，載具所能得到的僅只有發散定位資訊。因此本研究提出了一套使用自迴歸滑動平均模型(Autoregressive moving average model, ARMA model)之遞迴最小平方演算法(Recursive Least Square algorithm, RLS algorithm) 來抑制慣性感測元件輸出訊號之積分發散的問題，並藉由對載具於大地運動之慣性行為進行慣性定位進行誤差分析，結合 Kalman filter 技術來抑制慣性元件輸出訊號誤差，達成有效且精確提供載具於飛行、地面運動或海上航行設計時所須之定位及姿態資訊。在本論文中，將有效整合GPS/INS系統並置放於地面載具實測，顯示出慣性感測元件經由校正後能達成較佳的定位效果。

The Global Positioning System (GPS) is a space-based navigation system that provides location and time information in all weather conditions. The system is widely used in to military, civil, and commercial users around the world. However, GPS signals can be easily obscured by buildings or exceptional terrain. In general, inertial navigation system (INS) is integrated with GPS under the condition of GPS outage. The INS contains the accelerometer and gyroscope to measure the acceleration and angular velocity of the vehicle. However, a common question is the signal divergence of inertial measurement sensors due to the first and double integration process for obtaining the positioning and attitude message of vehicles. Generally speaking, the error divergence of inertial measurement unit (IMU) always comes from the drift rate of baseline of inertial devices and distribution of noise, and causes an unacceptable output. This thesis presents a method combined Autoregressive moving average model (ARMA model) with Recursive Least Square algorithm (RLS) and Kalman filter to suppress the errors of inertial measurement sensor outputs and derive an advanced estimator to eliminate divergence of positioning errors for offering accurate positioning messages to vehicles.

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