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系統識別號 U0026-1601201415503100
論文名稱(中文) 提升適用於移動製圖應用之多元感測器整合策略 與融合演算法
論文名稱(英文) INTEGRATION STRATEGIES AND ESTIMATION ALGORITHMS TO IMPROVE THE NAVIGATION ACCURACY OF LAND-BASED MOBILE MAPPING SYSTEMS
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系
系所名稱(英) Department of Geomatics
學年度 102
學期 1
出版年 103
研究生(中文) 楊成中
研究生(英文) Duong ThanhTrung
電子信箱 duong_trung2004@yahoo.com
學號 P68987015
學位類別 博士
語文別 英文
論文頁數 146頁
口試委員 指導教授-江凱偉
召集委員-張嘉強
口試委員-卓大靖
口試委員-陳國華
口試委員-韓仁毓
口試委員-郭重言
中文關鍵字 慣性導航系統  全球衛星定位系統  整合  估計  平滑 
英文關鍵字 INS  GPS  integration  estimation  smoothing 
學科別分類
中文摘要 近年來,移動測繪系統(Mobile mapping systems, MMS)因其能夠快速取得空間資訊而普遍被使用在許多應用中,這類系統通常透過整合慣性測量儀(Inertial Navigation System, INS)以及全球衛星定位系統(Global Positioning System, GPS),取得位置及方位等時變參數,達到無縫直接地理定位的功能。傳統的整合策略是以鬆耦合架構(Loosely coupled, LC)結合卡曼濾波器(Kalman filter, KF),但INS及GPS會受到許多誤差源的影響,尤其在某些對GPS不利的環境下,例如城市峽谷或隧道等,使用這類傳統的INS/GPS整合策略,且搭配的感測器及接收機等級較低時,定位精度受到誤差劣化的效應非常顯著。而透過使用高等級的硬體設備的確能提升整體定位的精度,但卻需要付出相當高的成本,甚至在某些條件下,受限於目前的資料整合技術,這些高成本的硬體設備仍然無法滿足使用者的需求,因此改善硬體的方式往往並不符合使用者的期望。本研究使用低精度且低成本的慣性測量儀及GPS接收機,透過改善INS/GPS整合策略並搭配進階的估計演算法,改善地面移動測繪系統的導航解精度。
針對INS/GPS整合架構,本研究提出一種改良的緊耦合架構,以克服目前緊耦合架構和鬆耦合架構的限制,並在GPS訊號脫落時,使用多種附加的輔助資訊及約制條件,以改善系統的可靠度。在估計演算法的部分,本研究使用模擬及實測資料,分析比較卡曼濾波器、無跡卡曼濾波器(Unscented Kalman filter, UKF)、粒子濾波器(Particle filter, PF)及混合濾波器(hybrid filter)的效能,並提出一種即時(on-line)的平滑器,改善現今的濾波技術及傳統平滑器的效能。最後透過不同的測試硬體及方案進行多次的戶外實地測試,評估並驗證本研究所提出之各種方法的效能。而分析結果顯示本研究提出的策略及演算法與傳統作法相比,能夠有效地改善系統效能。
英文摘要 Mobile mapping systems (MMSs) have been widely used to rapidly acquire spatial information. In such a system, the integration of the Inertial Navigation System (INS) and the Global Positioning System (GPS) is popularly applied as a direct geo-referencing system to seamlessly determine the time-variable position and orientation parameters. Given that both INS and GPS are affected by various error sources, these errors deteriorate the overall position and orientation accuracy of an integrated system that uses conventional INS/GPS integration strategies. These strategies include loosely coupled and common estimation strategies such as Kalman filter. These errors particularly occur when using low cost, small size inertial sensors and GPS receivers, or operating in GPS-hostile environments such as in urban canyons. The use of expensive equipment can improve the performance of the system, but also increases its overall cost. In some operating conditions, the high cost devices still do not fulfill the user’s requirements with the current dada fusion techniques. This study aims to improve the navigation solution accuracy of a land-based MMS utilizing a low cost inertial sensor and GPS receiver by focusing on integration strategies and advanced estimation algorithms.
For INS/GPS integration, a modified tightly coupled scheme was proposed to overcome the limitations of current tightly coupled and loosely coupled schemes. Various additional aids and constrains are discussed and applied to improve the robustness of the system in cases of GPS signal outages. For the estimation strategies, the efficiency of different filtering techniques such as KF, Unscented KF, particle filter, and hybrid filtering are analyzed using simulation and real data. A new estimation method called on-line smoothing is proposed to overcome the problems of filtering techniques and conventional smoothing algorithms. Various field tests using different devices and testing scenarios were conducted to evaluate the proposed methods. The analyzed results indicate that the proposed strategies and algorithms can significantly improve the performance of the system compared with conventional schemes. In various testing scenarios, the improvement of the modified TC over pure LC is about 60% and in GPS-denied environment, with the aid of additional constrains, the improvement of proposed scheme can reach to 90% over the scheme with INS/GPS.
論文目次 TABLE OF CONTENTS
ABSTRACT III
ACKNOWLEDGEMENT IV
TABLE OF CONTENTS V
LIST OF TABLES IX
LIST OF FIGURES X
GLOSSARY OF ACRONYMS XIII
CHAPTER 1: INTRODUCTION 14
1.1 Background and Problem Statement 14
1.2 Objectives and Contributions 23
1.3 Thesis Outline 24
CHAPTER 2: FUNDAMENTALS OF INTEGRATED NAVIGATION SYSTEMS 25
2.1 Reference Frames 25
2.1.1 Inertial Frame 25
2.1.2 Earth Centered Earth Fixed (ECEF) Frame 26
2.1.3 Geodetic Frame 26
2.1.4 Local Level Frame 27
2.1.5 Body Frame 27
2.2 Inertial Navigation System 28
2.2.1 Principle of the Inertial Navigation System 29
2.2.2 INS Mechanization 31
2.3 Inertial Sensor Error Model 35
2.3.1 Overview of the Inertial Sensor Error 35
2.3.2 Stochastic Process 37
2.4 GPS Signal and Measurements 39
2.4.1. Principle of GPS 40
2.4.2. GPS Signal 41
2.4.3. GPS Measurements 42
2.4.4. GPS Data Processing 44
2.4.5. Carrier Phase Processing in Kinematic Positioning 48
2.5 INS/GPS Integration 51
2.6 Additional Aiding Sources 54
2.6.1. Non-holonomic Constrain 54
2.6.2. ZUPT/ZIHR Update 55
CHAPTER 3: ESTIMATION STRATEGIES 57
3.1 General Concept of Estimation 57
3.2 Kalman Filter 59
3.3 Linearized Kalman Filter 60
3.4 Extended Kalman Filter 62
3.5 Particle Filter 63
3.6 Unscented Kalman Filter 66
3.7 Hybrid Estimation 69
3.8 Simulation of Estimation Strategies 72
3.8.1. Simulation on Linear Function 73
3.8.2. Simulation on Nonlinear Function 76
3.9 Smoothing 78
3.9.1. Two-filter Smoother 79
3.9.2. Rauch–Tung–Striebel off-line Smoothing 80
3.9.3. Online Smoothing 82
3.9.4. Output Rate of Online Smoothing for Real-time Application 84
3.9.5. Output Rate and Window Size of Online Smoothing versus Navigation Accuracy 85
CHAPTER 4: SYSTEM DESIGN 88
4.1 General Integrated System Design 88
4.2 Design of Modified Tightly Coupled Scheme 90
4.3 Real-time Integrated System 91
4.4 Model Design for Estimation 93
4.4.1. System Model 93
4.4.2. Measurement Model 97
4.5 Abnormal Measurement Elimination 98
4.5.1. Analytic Method for Rejecting Abnormal GPS Measurements 99
4.5.2. Statistical Method for Rejecting Abnormal GPS Measurements 101
4.6 Software Design 103
CHAPTER 5: TESTING AND DISCUSSIONS 106
5.1 Test on Estimation Algorithms 106
5.2 Test on Integration Strategies 110
5.2.1 Test on modified tightly coupled 110
5.2.2 Test on different modes of tightly coupled 116
5.2.3 Test additional aid 119
5.3 Test on Smoothing Strategies 123
CHAPTER 6: CONCLUSIONS AND FUTURE WORKS 133
6.1 Summary 133
6.2 Conclusions 134
6.3 Future Works 135
REFERENCE 136
APPENDIX 142
PUBLICATIONS 145
Journal Papers 145
Conference Papers 145
參考文獻 REFERENCE
Aggarwal, P., 2008. Hybrid Extended Particle Filter (HEPF) for INS/GPS Integrated System, Proceedings of ION GNSS 2008, Savannah, Georgia, September 16–19.
Aggarwal, P., Syed, Z., and El-Sheimy, N., 2009. Hybrid extended particle filter (HEPF) for integrated inertial navigation and global positioning systems, Measurement Science and Technology, 20(5).
Aggarwal, P., Syed, Z., Niu, X., and El-Sheimy, N., 2008. A standard Testing and Calibration for low cost MEMS Inertial sensor and Unit, Journal of Navigation, 611, pp. 323–336.
Arulampalam, M.S., Maskell, S., Gordon, N., and Clapp, T., 2002. A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking, IEEE Trans on Signal Processing, 50(2).
Bolić, M., Djurić, P.M., and Hong, S., 2004. Resampling Algorithms for Particle Filters: A Computational Complexity Perspective, EURASIP Journal on Advances in Signal Processing, 2004(15), pp. 2267–2277.
Bossler, J.D. and Novak, K., 1993. Mobile Mapping System: New Tools for the Fast Collection of GIS Information, GIS’93, Ottawa, Canada, March 23–25.
Bossler, J.D. and Schmidlay, R.W., 1997. Airborne Integrated Mapping System Promises Large-Scale Mapping Advancements. GIS World, 10(6), pp. 46–48.
Bradford, W.P. and James, J.S., 1996. Global Positioning System: Theory & Applications, American Institute of Aeronautics and Astronautics, Washington, DC, USA.
Brown, R.G. and Hwang, P.Y.C., 1992. Introduction to Random Signals and Applied Kalman Filtering, John Wiley & Sons Inc, New York, USA.
Chiang, K.W., Noureldin, A., and El-Sheimy, N., 2003. Multi-sensors integration using neuron computing for land vehicle navigation. GPS Solutions, 6(3), pp. 209–218.
Chiang, K.W., 2004. INS/GPS Integration Using Neural Networks for Land Vehicular Navigation Applications. Department of Geomatics Engineering, the University of Calgary, Calgary, Canada.
Chiang, K.W., Chang, H.W., Li, C.Y., and Huang, Y.W., 2009. An Artificial Neural Network Embedded Position and Orientation Determination Algorithm for Low Cost MEMS INS/GPS Integrated Sensors. Sensors, 9, pp.2586–2610.
Chiang, K.-W., Duong, T.T. and Liao, J.-K., 2013. The Performance Analysis of a Real-Time Integrated INS/GPS Vehicle Navigation System with Abnormal GPS Measurement Elimination. Sensors, 13, pp. 10599–10622.
Chiang, K.-W., Duong, T.T., Liao, J.-K., Lai, Y.-C., Chang, C.-C., Cai, J.-M. and Huang, S.-C., 2012. On-Line Smoothing for an Integrated Navigation System with Low-Cost MEMS Inertial Sensors. Sensors, 12(12), pp.17372–17389.
Chiang, K.W. and Huang, Y.W., 2008. An intelligent navigator for seamless INS/GPS integrated land vehicle navigation applications. Appl. Soft Comput. 8, pp. 722–733.
Der Merwe, V.R., Doucet, A., De Freitas, N. and Wan, E., 2000. The unscented particle filter, Advances in Neural Information Processing Systems (NIPS13), pp. 584–590.
Ding, W., Wang, J., Li, Y., Mumford, P., and Rizos, C., 2008. Time synchronization error and calibration in integrated GPS/INS systems. ETRI J. 30, pp. 59–67.
Dissanayake G., Sukkarieh S., Nebot, E., and Durrant-Whyte H., 2001. The aiding of a low cost, strapdown inertial unit using modeling constraints in land vehicle applications, IEEE Trans. on Robotics and Automation, 17, pp. 731–747.
Enge, P.K., 1994. The Global Positioning System: Signal, Measurement and Performance. International Journal of Wireless Information Network, 1(2), pp. 83-105.
Ellum, C.M. and El-Sheimy, N., 2001. Portable Mobile Mapping System, Proceeding of The 3rd Symposium on Mobile Mapping Technology, Cairo, Egypt, January 3–5.
El-Sheimy, N., 1996. The Development of VISAT-A Mobile Survey System for GIS Applications. Ph.D. Dissertation, Depertment of Geomatics Engineering, University of Calgary, Calgary, AB, Canada.
El-Sheimy, N., Abdel-Hamid, W., and Lachapelle, G., 2004. An Adaptive Neuro-Fuzzy Model for Bridging GPS Outages in MEMS-IMU/GPS Land Vehicle Navigation, Proceedings of ION GNSS 2004, Long Beach, California, September 21–24.
El-Sheimy, N., Chiang, K.W. and Noureldin, A., 2006. The Utilization of Artificial Neural Networks for Multisensor System Integration in Navigation and Positioning Instruments, IEEE Trans on Instrumentation and measurement, 55(5), pp. 1606 - 1615.
Farrell, J.A., Barth, M. and Andrews. A.P., 2001. The Global Positioning System and Inertial Navigation, McGraw-Hill, New York, USA.
Frei, E. and Beutler, G., 1990. Rapid Static Positioning Based on the Fast Ambiguity Resolution Approach FARA: Theory and First Results," Manuscripts Geodaetia, pp. 325–356.
Gao, Y., 2006. Precise Point Positioning and Its Challenges, Inside GNSS, pp. 16–18.
Gelb, A., 1974. Applied Optimal Estimation, The M.I.T. Press, Cambridge, Massachusetts, and London, England.
Gordon, N.J., Salmond, D.J., and Smith, A.F.M., 1993. Novel approach to nonlinear/non-Gaussian Bayesian state estimation, IEE Proceedings on Radar and Signal Processing, 140 (2), pp.107–113.
Groves, P. D., 2008. Principles of GNSS, inertial, and multi-sensor integrated navigation systems, Artech House, Boston, USA.
Hao, Z. and Huang, S., 2009. Integrated Navigation System Based on Differential Magnetic Compass and GPS, International Conference on Information Engineering and Computer Science, Wuhan, China December 19–20.
Hassibi, A. and Boyd, S., 1998. Integer parameter estimation in linear models with applications to GPS, IEEE Trans. Signal Processing, 46, pp. 2938–2952.
Haug, A.J., 2005. A Tutorial on Bayesian Estimation and Tracking Techniques Applicable to Nonlinear and Non-Gaussian Processes, MITRE technical report.
Huang, Y.W. and Chiang, K.W., 2009. Performance Analysis of Low Cost INS/GPS POS Systems for Land Based MMS Utilizing LC and TC Integration, ION GNSS 2009 Meeting, Savannah, Georgia, USA, September 22–25.
Hopfield, H.S., 1969. Two–quadratic tropospheric refractivity profile for correction satellite data. J. Geophys. Res. 74, pp. 4487–4499.
Jekeli, C., 2001. Inertial Navigation System with Geodetic Application, De Gruyter: Berlin, Germany, New York, USA.
Jing, Z., Stefan, K., Otmar, L., 2009. An Improved Low-Cost GPS/INS Integrated System Based on Embedded DSP Platform. In Proceedings of the 22nd International Technical Meeting of The Institute of Navigation, Anaheim, CA, USA, January 26–28, pp. 744–752.
Julier, S.J. and Uhlmann, J.K., 1997. New extension of the Kalman filter to nonlinear systems, Proceedings of the SPIE, pp. 182–193.
Kalman, R.E., 1960. A new research approach to Linear Filtering and Prediction Problem. J. Basic Eng, 82, pp. 35–45.
Khairi, A., Chris, H., and Terry, M., 2011. Integrating Low Cost IMU with Building Heading In Indoor Pedestrian Navigation. Journal of Global Positioning Systems, 10(1) pp. 30–38.
Klobuchar, J.A., 1987. Ionospheric time-delay algorithms for single-frequency GPS users. IEEE Trans. Aerosp. Electron. Syst, 23, pp. 325–331.
King, A., 1998. Inertial Navigation - 40 years of Evolution. GEC Review, 13(3).
Kubo, Y. and Wang, J., 2008. INS/GPS Integration Using Gaussian Sum Particle Filter, Proceedings of ION GNSS 2008, Savannah, Georgia, USA, September 16–19.
Lee, J.K., 2009. The estimation method for an Integrated INS/GPS UXO Geolocation System, Technical Report No. 493, The Ohio State University, Columbus, Ohio, USA.
Li, Y., Mumford, P., and Rizos, C., 2008. Performance of a Low-Cost Field Re-configurable Real-time GPS/INS Integrated System in Urban Navigation. In Proceedings of the 2008 IEEE/ION Position, Location and Navigation Symposium, Monterey, CA, USA, May 5-8, pp. 878–885.
Liu, C.Y., 2012. The Performance Evaluation of a Real-time Low-Cost MEMS INS/GPS Integrated Navigator with Aiding from ZUPT/ZIHR and Non-Holonomic Constraint for Land Applications. In Proceedings of the 25th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS), Nashville, TN, USA, September 17–21.
Liu, H., Nassar, S., and El-Sheimy, N., 2010. Two-Filter smoothing for accurate INS/GPS Land-Vehicle Navigation in Urban Center. IEEE Trans. Vehicular Technol, 59, pp. 4256–4276.
Misra, P. and Enge, P., 2001. Global Positioning System, Signals, Measurements and Performance, Ganga-Jamuna Press, Licoln, Massachusetts, USA.
Nassar. S., 2003. Improving the Inertial Navigation System (INS) Error Model for INS and INS/DGPS Applications, UCGE Reports Number 20183.
Niu, X., Nassar, S. and El-Sheimy, N., 2007. An accurate land-vehicle MEMS IMU/GPS navigation system using 3D auxiliary velocity updates. Journal of the Institute of Navigation, 54(3), pp. 177–188.
Park, M. and Gao, Y., 2008. Error and Performance Analysis of MEMS-based Inertial Sensors with a Low-cost GPS Receiver. Sensors, 8(4), pp. 2240–2261.
Park, S. K. and Suh, Y. S.A., 2010. Zero velocity detection algorithm using inertial sensors for pedestrian navigation systems, Sensors, 10(10), pp. 9163–9178.
Parkinson, B. W. and SpikkerJr, J.J., 1996. Global Psitioning system: Theory and Application, American of Aeronautics and Astronautics, Inc, Washington DC, USA.
Pham, T.M., 1992. Kalman Filter Mechanization for INS Airstart, IEEE AES Systems Magazine, pp. 3-11
Rau, J.-Y., Habib, A.F., Kersting, A.P., Chiang, K.-W., Bang, K.-I., Tseng, Y.-H., and Li, Y.-H. 2011. Direct Sensor Orientation of a Land-Based Mobile Mapping System. Sensors , 11, pp. 7243–7261.
Rauch, H., Tung, F., and Striebel, C., 1965. Maximum likelihood estimates of linear dynamic systems. AIAA J, 3, pp. 1445–1450.
Rogers, R.M., 2003. Applied Mathematics in Integrated Navigation Systems, Second Edition, AIAA, Virginia, USA.
Seeber, G., 2003. Satellite Geodesy. Walter de Gruyter, Berlin, New York, USA.
Shin, E.H., 2005. Estimation Techniques for Low-Cost Inertial Navigation, UCGE Reports Number 20156.
Shin, E.H., 2004. A Quaternion-Based Unscented Kalman Filter for the Integration of GPS and MEMS INS, Proceedings of ION GNSS 2004, Long Beach, California, September 21–24.
Sorenson, H. W. and Alspach, D. L., 1971. Recursive Bayesian estimation using Gaussian sums. Automatica, 7, pp. 465–479.
Spilker, J.J., 1980. GPS signal structure and performance characteristic. Journal of Navigation, 1(1), pp. 121-146.
Teunissen, P. J. G., 1994. A New Method for Fast Carrier Phase Ambiguity Estimation," Proceedings IEEE Position Location and Navigation Symposium PLAN94, Las Vegas, April 11–15, pp. 562–573.
Titterton, D.H., Weston, J.L., 2004. Strapdown inertial navigation technology, second edition, American Institute of Aeronautics and Astronautics, Reston, USA.
Toth, C.K., 2009. R&D of mobile LIDAR mapping and future trends, Proceedings of SPRS 2009, Baltimore, Maryland, March 9–13.
Van Der Merwe, R., Doucet, A., De Freitas,N., and Wan, E., 2000. The unscented particle filter, Advances in Neural Information Processing Systems (NIPS13), pp. 584–590.
Wendel, J., and Trommer, G.F., 2004. Tightly coupled GPS/INS integration for missile application. Aerosp. Sci. Technol., 8, pp. 627–634.
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