進階搜尋


 
系統識別號 U0026-2007201017064900
論文名稱(中文) 發展數位地圖嵌入式INS/GPS導航演算法
論文名稱(英文) The Performance Analysis of Map Embedded INS/GPS Fusion Algorithm for Seamless Vehicular Navigation in GPS Denied Environments
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系碩博士班
系所名稱(英) Department of Geomatics
學年度 98
學期 2
出版年 99
研究生(中文) 鄭蘭娟
研究生(英文) Lan-Chuan Cheng
學號 p6697410
學位類別 碩士
語文別 英文
論文頁數 98頁
口試委員 口試委員-卓大靖
口試委員-詹劭勳
指導教授-江凱偉
中文關鍵字 地圖匹配  GPS  INS 
英文關鍵字 Map Matching  GPS  INS 
學科別分類
中文摘要 在都市化發展迅速的台灣,人口密度正不斷地攀升,相對地,複雜的交通問題也應運而生。因此,有效且高可靠度的導航系統即相形重要。微機電(MEMS)慣性測量系統(INS)與全球定位系統(GPS)整合之無縫定位技術在可預見的未來GPS將成為導航系統的核心科技。然而,INS在沒有GPS資料更新時,無法持續保持高精度的導航解,使得位置誤差隨著時間而累積。因此,本文提出一個將地圖匹配演算法嵌入至傳統INS/GPS整合架構的方法,希望藉由這樣的方式,改善整合系統在GPS斷訊期間位置誤差的累積並改善整體的導航品質。最後藉由再一次的地圖匹配,加強此演算法定位結果在地圖資料庫上顯示的合理性。 透過以上的方式,以期得到無縫式且高精度的導航定位解。

本研究提出之演算法分為兩個部份:“地圖嵌入式導航系統”及“地圖匹配”。在“嵌入式導航系統”的部份,主要內容為修改傳統的鬆耦合整合架構,加入地圖匹配的約制,使用地圖匹配後的資料作適時的連續座標資料更新。經過處理後的導航解將會送至“地圖匹配”引擎繼續進行處理。“地圖匹配”中運用修改後鬆耦合架構預估之導航解,硬體中提供的載具航向角及地圖資料庫中的路網特性等資料進行相對的位置匹配,以期藉由這樣的方式降低導航解偏離路網的誤差量。

為了實現本文的演算法,在南台灣進行了實際資料的採集.這次實驗的地區包含了高雄及台南。實驗軌跡中包含了十字路口,平行路及高架橋等,初步的結果證明本文的演算法即使在沒有GPS訊號的區域也能在不增加硬體成本的基礎下有效地改善導航定位精度。

英文摘要 Taiwan is known for its dense population and the averaged ratio between the numbers of land vehicle and person reaches four, which means that people in Taiwan depends heavily on the transportation. In fact, traffic jam happens every day and becomes a nightmare to the people who live in the city. Therefore, a robust land vehicular navigation system that can provide the reliable geographic information could solve this problem effectively.

Global Positioning System (GPS) / Inertial Navigation System (INS) integrated systems are developed to be the major sensors of the navigation system for coming decades because of the stable output and the high accuracy in open-sky condition. However, an integrated navigation system can work under GPS denied environments, it also has some critical problems including the cost of inertial sensors and the time length during the GPS blockages. Therefore, in this study, a modified Map Matching (MM) algorithm is embedded to current INS/GPS fusion algorithm for enhancing the sustainability and accuracy of INS/GPS integration systems, besides, a cascade MM is also implemented to restrict the results obtained from the fusion system to keep the position of the vehicle being on the road. In principal, the proposed system is suitable for display and augmentation of real-time vehicular navigation system.

To validate the performance of the proposed MM embedded GPS/INS integration algorithm, two field tests were conducted in Kaohsiung and Tainan. The results indicate the proposed algorithms are able to improve the accuracy of positioning in GPS denied environments significantly with the use of two IMU/GPS integrated systems either in DGPS mode or SPP mode. The averaged improvement of the proposed algorithms exceeds 60% in terms of positioning accuracy and stability with the use of a low cost IMU integrated land vehicular navigation system. Consequently, the modified loosely coupled GPS/INS integration scheme with map derived positions can provide the most consistent navigation solutions with sufficient sustainability.
論文目次 摘要 I
Abstract II
Acknowledgement IV
Table of Contents V
List of Tables VII
List of Figures VIII
Glossary of Acronyms XI
Chapter 1: Overview and Introduction 1
1-1 Background 1
1-2 Problem Statement 3
1-3 Methodology 5
1-4 Thesis Outline 6
Chapter 2: The Introduction of GPS/INS Integration System 9
2-1 Coordinate Systems and Transformations 9
2-2 Global Positioning System 14
2-2-1 Overview of GPS 14
2-2-2 GPS Fundamental 16
2-2-3 Differential GPS 17
2-2-4 GPS Error 19
2-3 Inertial Navigation System 21
2-4 GPS/INS Integration System 29
2-5 Extended Kalman Filter 34
Chapter 3: Map Matching 39
3-1 Overview of MM 40
3-2 Proposed MM Procedure 46
3-2-1 Position Fix 47
3-2-2 Boundary Setting 48
3-2-3 Heading Constrained 48
3-2-4 Nearest point searching 50
3-2-5 Error detection 51
3-3 Proposed Algorithm 51
3-3-1 Map Embedded INS/GPS Fusion Algorithm 52
3-3-2 Cascade Map Matching 55
Chapter 4: Results and Discussion 56
4-1 Hardware and Condition of the Tested System 56
4-2 Performance Analysis 60
4-2-1 Kaohsiung Trail 60
4-2-2 Tainan trail 65
4-3 The Analysis of Specific Regions 71
4-3-1 Kaohsiung Trail 71
4-3-2 Tainan Trail 74
4-4 Error Analysis of Proposed Algorithm 76
Chapter 5: Conclusions and Recommendations 84
5-1 Conclusions 84
5-2 Recommendations 85
Reference 87
Appendix A 91
Appendix B 92
Appendix C 93
參考文獻 Reference

1. Abdel-Hamid, W., Abdelazim, T., El-Sheimy, N., and Lachapelle, G., Improvement of MEMS-IMU/GPS performance using fuzzy modeling, GPS Solution, vol. 10, pp. 1-11., 2006.
2. Bar-Itzhack, I.Y., and Berman, N., Control Theoretic Approach to Inertial Navigation System, AIAA Journal of Guidance, Control & Dynamics, Vol. 11, pp. 237-245, 1988.
3. Bishop, C.M., Neural Networks for Pattern Recognition, Oxford, 1995.
4. Brown, R.G., and Hwang, P.Y.C., Introduction to Random Signals and Applied Kalman Filtering, John Wiley & Sons, 1997.
5. Bullock, J.B., A Prototype Portable Vehicle Navigation System Utilizing Map Aided GPS, University of Calgary, Calgary, Canada, UCGE Reports No.20081, 1995.
6. Chiang, K.W., The Utilization of Single Point Positioning and a Multi-Layers Feed-forward Network for INS/GPS Integration, Institute of Navigation (ION) GPS, Oregon Convention Center, Portland, Oregon, USA, 2003.
7. Chiang, K.W., INS/GPS Integration Using Neural Networks for Land Vehicular Navigation Applications, PhD Thesis, Department of Geomatics Engineering, the University of Calgary, Calgary, Canada, UCGE Report No. 20209, 2004.
8. Chiang, K.W., and Huang, Y.W., An Intelligent Navigator for Seamless INS/GPS Integrated Land Vehicle Navigation Applications. Applied Soft Computing, Volume 8, Issue 1, January 2008, Pages 722-733, 2008.
9. Crista-Interface/Operation Document, Crista Inertial Measurement Unit (IMU) Interface / Operation Document, A Cloud Cap Technology Inc, 2004.
10. Drane, C., and Rizos, C., Positioning Systems in Intelligent Transportation Systems, Artech House, Boston, London, 1998.
11. El-Rabbany, A., Introduction to GPS: The Global Positioning System, Artech House, Boston, London, 2002.
12. El-Sheimy, N., Introduction to Inertial Navigation, ENGO699.71 Lecture Notes, Geomatics department, University of Calgary, Calgary, Canada, 2002.
13. El-Sheimy, N., The Potential of Partial IMUs for Land Vehicle Navigation. Inside GNSS, 2008.
14. French, R.L., Map Matching Origins, Approaches and Applications, Proc. Second International Symposium on Land Vehicle Navigation, pp. 97-116, 1989.
15. Godha, S., Performance Evaluation of Low Cost MEMS-Based IMU Integrated with GPS for Land Vehicle Navigation Application, Department of Geomatics Engineering, MSc Thesis, University of Calgary, Calgary, Canada, UCGE Report No. 20239, 2006.
16. Goodall, C., El-Sheimy, N., and Chiang, K.W., The development of a GPS/MEMS INS Intergrated System Utilizing a Hybrid Provessing Architecture, Institute of Navigation GNSS 2005 proceedings, Long Beach, California, 2005.
17. Grewal, M.S., Weill, L.R., and Andrews, A.P., Global Positioning Systems, Inertial Navigation and Integration 2nd, A John Wiley & Sons.
18. Grewal, M.S., and Andrews, A.P., Kalman Filtering – Theory and Practice Using MATLAB 2nd , A John Wiley & Sons.
19. Hellmann, M., 2001, Fuzzy logic Introduction, France, 2007.
20. Hide, C., and Moore, T., GPS and Low Cost INS Integration for Positioning in the Urban Environment, ION GNSS 18th International Technical Meeting of the Satellite Division, Long Beach, CA, 2005.
21. Hou, H., Modeling Inertial Sensors Errors Using Allan Variance, MSc Thesis, Department of Geomatics Engineering, University of Calgary, Canada, UCGE Report No. 20201, 2004.
22. Kaplan, E.D., Understanding GPS: Principles and Applications, Artech House, Norwood, MA, 1996.
23. Kong, X., Inertial Navigation System Algorithms for Low Cost IMU, PhD dissertation, University of Sydney, Australia, 2000.
24. Prasad, R., and Ruggieri, M., Applied Satellite Navigation Using GPS, GALILEO, and Augmentation Systems, pp. 39-56, 2005.
25. Quddus, M.A., Ochieng, W.Y., Zhao, L., and Noland, R.B., A general map matching algorithm for transport telematics applications, GPS Solutions, vol. 7, pp. 157-167, 2003.
26. Schwarz, K.P., and Wei, M., INS/GPS Integration for Geodetic Applications: Lecture Notes ENGO 623, Department of Geomatics Engineering, The University of Calgary, Calgary, Canada, 2000.
27. Shin, E.H., Accuracy Improvement of Low Cost INS/GPS for Land Applications, MSc Thesis, Department of Geomatics Engineering, University of Calgary, Calgary, Canada, 2001.
28. Shin, E., and El-Sheimy, N., An Unscented Kalman Filter for In-Motion Alignment of Low Cost IMUs, proceedings of Position Location and Navigation Symposium IEEE, 2004.
29. Stephen, J., Development of A Multi-Sensor GNSS Based Vehicle Navigation System, MSc Thesis, Deaprtment of Geomatics Engineering, University of Calgary, Calgary, Canada, 2000.
30. Winter, M., Application of artificial neural networks to map matching for GPS navigation, Diploma Thesis, Dresden University of Technology, Germany and University of Glamorgan, UK, 2002.
31. Winter, M., and Taylor, G., Modular Neural Networks for Map-Matching GPS Positioning, School of Computing, University of Glamorgan, UK, 2003.
32. Yu, M., Li, Z., Chen, Y., and Chen, W., Improving Integrity and Reliability of Map Matching Techniques, Journal of Global Positioning Systems, vol. 5, pp. 1-2: 40-46, 2004.
33. Zhao, Y., Vehicle Location and Navigation Systems, Artech House, Boston, London, pp. 83-102, 1997.
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2010-07-22起公開。
  • 同意授權校外瀏覽/列印電子全文服務,於2010-07-22起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw