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研究生:李怡萱
研究生(外文):Yi-HsuanLee
論文名稱:三維數位資訊輔助INS/GPS無縫式導航演算法之效益分析
論文名稱(外文):The Performance Analysis of 3D Digital Information Aided Algorithm for Seamless INS/GPS Integrated Navigation System
指導教授:江凱偉江凱偉引用關係
指導教授(外文):Kai-Wei Chiang
學位類別:碩士
校院名稱:國立成功大學
系所名稱:測量及空間資訊學系碩博士班
學門:工程學門
學類:測量工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:74
中文關鍵詞:慣性導航系統全球導航定位系統地圖匹配汽車導航
外文關鍵詞:INSGPSMap MatchingVehicular Navigation
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近年來,導航技術在即時車載系統及後處理資料分析等皆有廣泛的應用,尤其在科技持續發展進步的推演下,全球衛星定位系統(GPS)儼然在導航系統發展中,佔有舉足輕重的地位。然而都市化的趨勢在全世界迅速延展,不論新興都市或已發展城市興建高樓大廈及高架交通建設的比例逐日漸增,進而對GPS在車載導航系統的訊號接收上有一定的影響。純GPS導航因高樓或高架建設使車載導航系統產生的訊號失鎖及多路徑效應確實地影響了GPS導航的準確性。藉由整合慣性導航系統(INS),可有效的增加GPS在短時間訊號失鎖時的動態定位精度,1960年代發展的卡曼濾波器(KF)亦成為整合INS/GPS最常使用的整合式演算法。然INS的價格受其內部慣性測量元件(IMU)精度的等級有劇烈的差異。在低成本導航系統之市場需求下,整合GPS及微機電系統(MEMS) 等級的IMU是現今較普遍使用於車載整合式慣性導航系統。因此,為了增進低成本整合式車載導航系統的準確性,多種演算法因運而生。
地圖匹配(MM)定義為將導航獲得之汽車座標擬合至實際道路上最近點位的位置以符合車載導航系統進一步準確預測目的地最短路徑之用。隨著圖資收集的完整性及資料庫擴展的廣泛性,MM的應用可從車載導航系統廣泛至日常生活中隨處可見之行動裝置的軟體輔助資訊。儘管MM演算法經歷了幾十年的發展,但目前尚無法克服汽車在面臨同一道軌跡上同時有平面道路及有高架橋時的準確辨識。
本研究利用MEMS等級之INS/GPS整合性導航系統,以三維數位資訊後處理演算法輔助及增加導航系統的汽車定位精度,並特別針對高程的擬合精度作探討,進一步討論該演算法在辨識車行高架橋上下之可行性。本研究的成果顯示該演算法運用在兩種不同GPS解算模式及兩種不同等級的IMU組合下皆有一定的進步幅度,證實該演算法對鬆耦合架構下的INS/GPS導航整合系統增添很大的穩定性。

Nowadays, the importance of navigation technology is increasing and widely applied not only in real time vehicles’ navigation but kinematic positioning for post processing circumstances. The Global Positioning System (GPS) plays an important role in the navigation purpose recently because of the stability and universality providing the position information, whereas the GPS can’t make precise position information due to the blockage of signal. For this reason, there are many cases of enhancements for compensating the limitation of GPS system, one of them is combining the aiding sensor to support in GPS denied area. The most famous and useful one is the Inertial Navigation System (INS) fusion technique that enhances the GPS-only navigation system by integrating additional instruments. The INS/GPS fusion system improves the navigation output of GPS stand-alone condition by including the velocity and attitude information to realize more reasonable navigation results while the urbanized cities caused the worse GPS navigation outputs.
Along with the optimal integrated scheme of Kalman Filter (KF), the GPS is able to work with the INS while the integrated navigation equations were not displayed as linear. In order to realize the non-linear process of integrating INS/GPS, the Extended Kalman Filter (EKF) was in the usage for INS/GPS integration within the past 10 years.
Considering the general usage of real-time navigation for vehicle navigation system, the Map Matching (MM) had been broadly used in the vehicular systems producing the expected direction for vehicle destination based on the shortest path theory and matched with the recording of road database for drivers to follow. However, the matching algorithm cannot recognize the path accurately for the under or on highways conditions due to the navigation outage affected by urban canyons of buildings and urban constructions.
In this study, a 3D digital information aided algorithm is embedded to current INS/GPS fusion algorithms for enhancing the sustainability and accuracy of INS/GPS integration systems, especially the height component. In addition, this study proposes an effective solution to the limitation of current commercial vehicular navigation systems where they fail to distinguish whether the vehicle is moving on the elevated highway or the road under it because those systems don’t have sufficient height resolution. The preliminary results indicate the proposed algorithms are able to improve the accuracy of positional components in GPS denied environments significantly with the use of INS/GPS integrated systems in both DGPS and SPP mode. Consequently, the modified loosely coupled INS/GPS integration scheme with matching positions can provide the most consistent navigation solutions with sufficient sustainability.
中文摘要 I
Abstract III
Acknowledgement V
Table of Contents VII
List of Tables X
List of Figures XI
Glossary of Acronyms XIV
Chapter 1 Introduction and Overview 1
1-1 Background 1
1-2 Motivation 2
1-3 Methodology 4
1-4 Thesis Outline 5
Chapter 2 INS/GPS Integration System 7
2-1 Reference Frames and Transformations 7
2-2 Global Positioning System 13
2-3 Inertial Navigation System 17
2-4 INS/GPS Integrated Systems 25
Chapter 3 Conventional and INS/GPS Integration Algorithm 29
3-1 Kalman Filter 29
3-2 Extended Kalman Filter 33
3-3 Smoother 34
Chapter 4 3D Digital Information Aided Algorithm 38
4-1 Overview of MM 38
4-2 3D Digital Information Aided Algorithm Procedure 41
4-2-1 Initial Fixed 41
4-2-2 Boundary Setting 42
4-2-3 Heading Constrained 43
4-2-4 Nearest Position Searched 44
4-2-5 Height Constrained 45
4-2-6 Error Fixed 45
Chapter 5 Experimental Setup and Architecture 47
5-1 The Proposed Algorithm Architecture 47
5-2 Conditions of Field Test 51
Chapter 6 Results and Discussions 55
6-1 The Performance Analysis of Proposed Algorithm 55
6-2 The Extended Outage Scenario 63
Chapter 7 Conclusions and Future Works 67
7-1 Conclusions 67
7-2 Recommendations 68
References 70
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