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研究生:簡彥豪
研究生(外文):Yan-Hao Chien
論文名稱:利用GPS載波相位觀測執行時間同步校正
論文名稱(外文):Study of Time Synchronization and Calibration Using GPS Carrier Phase
指導教授:黃國興黃國興引用關係
指導教授(外文):Guo-Shing Huang
學位類別:碩士
校院名稱:國立勤益技術學院
系所名稱:資訊與電能科技研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2006
畢業學年度:94
語文別:中文
論文頁數:87
中文關鍵詞:全球衛星定位系統載波相位可拓控制器可拓類神經網路時間同步校正頻率穩定
外文關鍵詞:GPSCarrier PhaseExtension ControllerExtension Neural NetworkTime Synchronization and CalibrationFrequency Stability
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本論文主要目的是利用全球衛星定位系統載波相位來做時間的校準,以銣原子鐘假設為衛星的原子鐘,再透過單頻的接收器所產生1PPS的信號作比較,就可以估算出傳輸延遲的誤差量。及利用全球衛星定位系統載波相位觀測來執行頻率穩定及時間的校正。根據參考文獻大部分的作法都是使用兩套GPS接收器作觀測,利用二次差來執行頻率穩定及座標定位。本文提出僅使用單機雙頻GPS接收器,加上新發展的可拓控制器類神經網路演算法一樣可以做到頻率穩定及座標定位。首先使用一套參考銣原子鐘當作衛星原子鐘的振盪頻率,同時作為調整GPS接收器振盪頻率的標準。透過我們所設計的可拓控制器來調整接收器的振盪頻率同步於參考原子鐘的振盪頻率。完成頻率穩定與時間同步不僅能完成時間的校準,更能提高測量或導航定位精度。經使用可拓控制器實驗結果證實平均定位精度誤差可由2.1公尺改善至0.73公尺,而平均時間誤差可由 秒改善至 秒。使用可拓類神經網路實驗結果證實平均定位精度誤差可由2.1公尺改善至0.7公尺,而平均時間誤差可由 秒改善至 秒。驗證了初始的想法之正確性、可行性與強健性。
The main purpose of this paper is to calibrate the time by using GPS carrier phase measurements. The rubidium atom clock supposes the atom clock of the satellites, then we can obtain the transmission error via the signal of 1PPS of the GPS receiver single frequency observation. In this paper, we utilize GPS carrier phase measurements to calibrate the time synchronization and frequency stability. So far the most papers are to utilize two GPS receivers measurement, and perform the frequency stabilizing and the vehicle positioning with the double differences. This paper proposes only using a dual frequency GPS receiver and a novel extension controller neural network algorithm, can achieve the frequency stabilizing and coordinate positioning. At first, we select the oscillation frequency of the rubidium atom clock as the oscillation frequency of the atom clock of satellites as the criterion for adjusting the GPS receiver oscillator’s frequency. It is adjusted that the receiver’s oscillation frequency synchronizes the reference atomic clock the oscillation frequency by the extension controller and extension neural network that we design. To finish the frequency stabilizing and time synchronization, can calibrate the time and improve the position precision on the vehicle even more. Through the experimental results, the average position precision error can be improved from about 2.1 meters to about 0.73 meters using the extension controller. The average time error can be improved from about seconds to about seconds. Using the experimental results of extension neural network to verify the average position precision error can be improved from about 2.1 meters to about 0.7 meters. The average time error can be improved from about seconds to about seconds. It is verified that the system is satisfied the exactness, feasibility and robustness of the initial concept.
中文摘要 ------------------------------------------- i
英文摘要 ------------------------------------------- ii
誌謝 ----------------------------------------------- iv
目錄 ----------------------------------------------- v
表目錄 -------------------------------------------- vii
圖目錄 ------------------------------------------- viii
符號說明 ------------------------------------------- x
第一章 緒論----------------------------------------- 1
1.1 前言------------------------------------------- 1
1.2 研究動機------------------------------------- 2
1.3 研究目的------------------------------------- 4
1.4 文獻回顧---------------------------------------- 5
1.5 論文架構---------------------------------------- 8
第二章 GPS衛星定位系統-------------------------------- 9
2.1 GPS簡介----------------------------------------- 9
2.2 GPS定位原理------------------------------------- 12
2.2.1 GPS觀測量------------------------------------ 13
2.2.2 GPS的導航資料--------------------------------- 19
2.2.3 GPS定位計算----------------------------------- 26
2.3 定位誤差來源及分析------------------------------- 29
2.3.1 衛星方面的誤差-------------------------------- 30
2.3.2 傳播媒介誤差---------------------------------- 31
2.3.3 接收器誤差------------------------------------ 35
第三章 控制理論推導與分析----------------------------- 37
3.1 Adaline類神經網路--------------------------- 37
3.2 可拓理論---------------------------------------- 39
3.2.1 物元的概念------------------------------------ 40
3.2.2 可拓集合的定義-------------------------------- 42
3.2.3 物元可拓集合---------------------------------- 43
3.2.4 關聯函數------------------------------------- 44
3.3 類神經網路------------------------------------- 50
3.4 可拓控制器------------------------------------- 51
3.4.1 可拓控制器的架構------------------------------ 51
3.4.2 可拓控制器的演算法---------------------------- 52
3.5 可拓類神經網路--------------------------------- 54
3.5.1 可拓類神經網路架構----------------------------- 54
3.5.2 可拓類神經網路的演算法------------------------- 55
第四章 系統架構------------------------------------- 58
4.1 Adaline類神經網路------------------------------ 58
4.1.1 Adaline類神經網路系統架構---------------------- 58
4.1.2 Adaline類神經網路理論推導----------------- 59
4.2 可拓控制器--------------------------------- 61
4.2.1 可拓控制器系統架構------------------------ 61
4.2.2 可拓控制器理論推導------------------------ 62
4.3 可拓類神經網路----------------------------- 64
4.3.1 可拓類神經網路系統架構-------------------- 64
4.3.2 可拓類神經網路理論推導-------------------- 65
第五章 實際測試實驗結果與分析------------------------ 68
5.1 利用1PPS得知傳輸誤差--------------------------- 68
5.2 時間校正的實驗結果----------------------------- 72
5.2.1 資料解算與分析------------------------------- 73
5.2.2 可拓控制器的應用----------------------------- 75
5.2.3 可拓類神經網路的應用-------------------------- 77
第六章 結論與未來研究方向---------------------------- 80
6.1 結論----------------------------------------- 80
6.2 未來研究方向---------------------------------- 81
參考文獻 ----------------------------------------- 84
作者簡介 ----------------------------------------- 87
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