碩士 / 國立成功大學 / 系統及船舶機電工程學系 / 104 / 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.
| Identifer | oai:union.ndltd.org:TW/104NCKU5345037 |
| Date | January 2016 |
| Creators | Yi-HuaHsu, 徐逸驊 |
| Contributors | Yung-Yu Chen, 陳永裕 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 60 |
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