論文使用權限 Thesis access permission:校內校外均不公開 not available
開放時間 Available:
校內 Campus:永不公開 not available
校外 Off-campus:永不公開 not available
論文名稱 Title |
感測器動態追蹤與方位估測: 基於交互式多模演算法之濾波器設計 Orientation Estimation and Sensor Motion Tracking: An IMM Algorithm-Based Filter Design |
||
系所名稱 Department |
|||
畢業學年期 Year, semester |
語文別 Language |
||
學位類別 Degree |
頁數 Number of pages |
105 |
|
研究生 Author |
|||
指導教授 Advisor |
|||
召集委員 Convenor |
|||
口試委員 Advisory Committee |
|||
口試日期 Date of Exam |
2010-07-06 |
繳交日期 Date of Submission |
2010-08-02 |
關鍵字 Keywords |
交互式多模演算法、慣性感測器、四元數、方位估測 orientation estimation, quaternion, IMM, inertial sensor |
||
統計 Statistics |
本論文已被瀏覽 5753 次,被下載 0 次 The thesis/dissertation has been browsed 5753 times, has been downloaded 0 times. |
中文摘要 |
在本論文中,我們提出基於交互式多模演算法(Interacting Multiple Model Algorithm) 之濾波器設計架構,透過慣性感測器的量測值,針對物體在三度空間中的 方位轉動,進行即時動態方位估測與追蹤。對於感測器狀態的描述及方位估測,感測器 依其量測訊號特性會有不同的使用方式,陀螺儀雖具有高靈敏度的特性,然而會隨著時 間而產生偏差量;加速計與磁力計雖靈敏度較低,但較無偏差量的問題。陀螺儀通常為 主要感測器,而磁力計與加速計的結合使用則為輔助感測器。透過空間轉動的數學描述 和感測器量測訊號特性的處理,相關的研究常結合主要和輔助感測器資訊進行估測器設 計。基於資訊整合概念,我們探討單獨使用的慣性感測器的量測值,透過多模式的交互 演算,在不同方位估測和運動狀態中呈現各感測器的優點。我們提出了一個基於交互式 多模演算法之訊號處理架構,結合三個平行排列、分別處理加速計、磁力計與陀螺儀量 測訊號的卡爾曼濾波器。因為加速計無法感測環繞垂直軸的轉動,而磁力計則只能感測 環繞垂直軸的轉動,導致平行排列交互式多模演算架構估測準確度的降低。提出的架構 在交互演算的量測殘餘值計算程序中,提供以陀螺儀量測為主的卡爾曼濾波器預測值至 另外兩個濾波器輔助修正權重機率的計算。另外,針對文獻中以四元數為主的「三合 一」擴展式卡爾曼濾波器方法,由於估測器直接結合處理三種感測器的訊號,使得輔助 感測器和主要感測器的缺點彼此影響,因此我們探討以單獨陀螺儀為主的擴展式卡爾曼 濾波器的特性,並提出一個平行多模的演算架構,交互處理兩種擴展式卡爾曼濾波器的 估測結果。對於感測器方位估測與動態追蹤,我們發現交互式多模演算法除了具有融合 加速計、磁力計與陀螺儀量測訊號分別處理的三個估測器模式的估測結果外,亦能有效 融合「三合一」與單獨陀螺儀的兩個估測器模式的估測結果,且透過具有彈性的模型權 重計算進而取得最佳的估測值。我們針對所提出的基於交互式多模演算法之卡爾曼濾波 器以及擴展式卡爾曼濾波器的估測結果與設計程序進行比較,並對於感測器在訊號整合 上的結果進行討論分析。電腦的模擬結果驗證了我們所提出的估測器設計概念,並證明 交互式多模演算程序具有能降低整體估測錯誤的能力。 |
Abstract |
In the thesis, we present the structures of interacting multiple model (IMM) algorithm-based filter design for real-time motion orientation estimation and tracking by using inertial sensor measurements in three-dimensional space. The major sensor such as gyroscope, though has high-sensitivity characteristics, suffers from bias build-up and error drift over time. The complementary sensors such as accelerometer and magnetometer, on the other hand, have low sensitivity, but do not suffer from bias problems. By using individual inertial and magnetic sensors, measurements of multiple modes can be interactively computed. The IMM based designs show the advantages of weighting individual sensors in different motion states. We propose a signal processing architecture based on the IMM algorithm. It is composed of three parallel Kalman filters (KFs), each deals with measured signals from accelerometer, magnetometer and gyroscope, respectively. The accelerometer cannot effectively sense the rotation around the vertical axis; while the magnetometer can only sense the rotation around vertical axis. Therefore, estimation accuracy with the parallel filtering arrangement of the IMM algorithm-based structure may be affected. A scheme using the residual signal, which is computed in the IMM, provides the information of gyroscope-based KF to the other two filters for feasible calculation of update weights. Related research also usually combined the information of major and complementary sensors in estimator designs. In the literature, existing “Triad” methods with quaternion-based extended Kalman filter (EKF), process the measurements from major and complementary sensors. To compensate the functions, we propose to use a gyroscope-based EKF and a Triad EKF in forming a parallel multiple model-based structure. The analysis and performance evaluation shows advantages and disadvantages of using EKFs and KFs in IMM-based filtering approachs. Simulation results validate the proposed estimator design concept, and show that the scheme is capable of reducing the overall estimation errors by flexible computation of model weights. |
目次 Table of Contents |
ACKNOWLEDGEMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . i 中文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii ABSTRACT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii TABLE OF CONTENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi 1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1 Overview of Inertial and Magnetic Sensing in Orientation Estimation 1 1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.3 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2 INERTIAL SENSING AND ORIENTATION ESTIMATION . . . . . . . 9 2.1 Characteristics of Inertial and Magnetic Sensors . . . . . . . . . . . 9 2.2 Sensing Signal Model . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2.3 Quaternion-Based Filtering for Orientation Estimation . . . . . . . 11 2.3.1 Quaternion Method Approach . . . . . . . . . . . . . . . . . 11 2.3.2 Bias Estimation of Gyroscope . . . . . . . . . . . . . . . . . 13 2.3.3 Orientation Determination Algorithms . . . . . . . . . . . . 13 2.3.4 Kalman Filtering for Orientation Estimation . . . . . . . . . 21 2.4 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3 PROPOSED IMM-BASED FILTERING USING INDIVIDUAL SENSORS 29 3.1 The KF Using Measurements of Individual Sensors in IMM . . . . . 29 3.1.1 The Individual Sensing KF Design . . . . . . . . . . . . . . 29 3.1.2 Comparison on Individual Sensors of KF . . . . . . . . . . . 32 3.2 IMM-Based Filtering Approach for Individual KF . . . . . . . . . . 33 3.2.1 IMM-AG Structure . . . . . . . . . . . . . . . . . . . . . . . 33 3.2.2 IMM-MG Structure . . . . . . . . . . . . . . . . . . . . . . . 41 3.2.3 IMM-AMG Structure . . . . . . . . . . . . . . . . . . . . . . 46 3.3 Improvement of Proposed IMM-Based KF . . . . . . . . . . . . . . 51 3.3.1 Proposed IMM-AG . . . . . . . . . . . . . . . . . . . . . . . 55 3.3.2 Proposed IMM-MG . . . . . . . . . . . . . . . . . . . . . . . 56 3.3.3 Proposed IMM-AMG . . . . . . . . . . . . . . . . . . . . . . 61 3.3.4 Effects in Improved Method of Proposed IMM-Based Filtering 70 4 PROPOSED IMM-BASED EXTENDED KALMAM FILTERING . . . . 71 4.1 Multiple Models for Orientation Estimation . . . . . . . . . . . . . 71 4.1.1 Gyroscope-Based EKF Design . . . . . . . . . . . . . . . . . 71 4.1.2 Comparison on QBEKF and GBEKF . . . . . . . . . . . . . 72 4.2 An IMM-Based Filtering Approach . . . . . . . . . . . . . . . . . . 73 4.2.1 The Performance of IMMEKF . . . . . . . . . . . . . . . . . 75 4.3 A Variety of IMM-Based QBEKFs . . . . . . . . . . . . . . . . . . 76 4.4 Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 5 ANALYSIS AND PERFORMANCE EVALUATION . . . . . . . . . . . 81 5.1 Comparison on IMM-Based KFs/EKFs in Orientation Estimation . 81 5.2 Effects of Different Sensor Approaches . . . . . . . . . . . . . . . . 82 5.2.1 Integration of Accelerometer and Gyroscope . . . . . . . . . 82 5.2.2 Integration of Magnetometer and Gyroscope . . . . . . . . . 83 5.2.3 Integration of Accelerometer, Magnetometer and Gyroscope 83 6 CONCLUSIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 REFERENCES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 |
參考文獻 References |
[1] G. Dissanayake, S. Sukkarieh, E. Nebot, and H. Durrant-Whyte, “The aiding of a low-cost strapdown inertial measurement unit using vehicle model constraints for land vehicle applications,” IEEE Transactions on Robotics and Automation, vol. 17, no. 5, pp. 731–747, 2001. [2] S. Felter and N. Wu, “A relative navigation system for formation flight,” IEEE Transactions on Aerospace and Electronic Systems, vol. 33, no. 3, pp. 958–967, 1997. [3] R. Toledo-Moreo, M. Zamora-Izquierdo, B. Ubeda-Miarro, and A. Gomez- Skarmeta, “High-integrity IMM-EKF-based road vehicle navigation with lowcost GPS/SBAS/INS,” IEEE Transactions on Intelligent Transportation Systems, vol. 8, no. 3, pp. 491–511, 2007. [4] P.-M. Lee, B.-H. Jun, K. Kim, J. Lee, T. Aoki, and T. Hyakudome, “Simulation of an inertial acoustic navigation system with range aiding for an autonomous underwater vehicle,” IEEE Journal of Oceanic Engineering, vol. 32, no. 2, pp. 327–345, 2007. [5] J. S. Jang and D. Liccardo, “Small UAV automation using MEMS,” IEEE Aerospace and Electronic Systems Magazine, vol. 22, no. 5, pp. 30–34, 2007. [6] S. You, U. Neumann, and R. Azuma, “Orientation tracking for outdoor augmented reality registration,” IEEE Computer Graphics and Applications, vol. 19, no. 6, pp. 36–42, 1999. [7] S. Polak, Y. Barniv, and Y. Baram, “Head motion anticipation for virtualenvironment applications using kinematics and EMG energy,” IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans, vol. 36, no. 3, pp. 569–576, 2006. [8] X. Yun and E. R. Bachmann, “Design, implementation, and experimental results of a quaternion-based Kalman filter for human body motion tracking,” IEEE Transactions on Robotics, vol. 22, no. 6, pp. 1216–1227, 2006. [9] A. Sabatini, “Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing,” IEEE Transactions on Biomedical Engineering, vol. 53, no. 7, pp. 1346–1356, 2006. [10] E. Bachmann, X. Yun, and A. Brumfield, “Limitations of attitude estimnation algorithms for inertial/magnetic sensor modules,” IEEE Robotics and Automation Magazine, vol. 14, no. 3, pp. 76–87, 2007. [11] J. K. Lee and E. Park, “A fast quaternion-based orientation optimizer via virtual rotation for human motion tracking,” IEEE Transactions on Biomedical Engineering, vol. 56, no. 5, pp. 1574–1582, 2009. [12] X. Yun, E. Bachmann, and R. McGhee, “A simplified quaternion-based algorithm for orientation estimation from earth gravity and magnetic field measurements,” IEEE Transactions on Instrumentation and Measurement, vol. 57, no. 3, pp. 638–650, 2008. [13] M. Shuster, “A survey of attitude representations,” The Journal of the Astronautical Sciences, vol. 41, no. 4, pp. 439–517, October 1993. [14] J. Chou, “Quaternion kinematic and dynamic differential equations,” IEEE Transactions on Robotics and Automation, vol. 8, no. 1, pp. 53–64, 1992. [15] N. H. Q. Phuong, H.-J. Kang, Y.-S. Suh, and Y.-S. Ro, “Study on orientation estimation with three different representations,” in Proceedings of the International Symposium on Electrical and Electronics Engineering, Oct 2007, pp. 80–88. [16] D. Choukroun, I. Bar-Itzhack, and Y. Oshman, “Novel quaternion Kalman filter,” IEEE Transactions on Aerospace and Electronic Systems, vol. 42, no. 1, pp. 174–190, 2006. [17] M. Sato and J. Aggarwal, “Estimation of position and orientation from image sequence of a circle,” in Proceedings of IEEE International Conference on Robotics and Automation, vol. 3, 1997, pp. 2252–2257. [18] X. Yun, M. Lizarraga, E. Bachmann, and R. McGhee, “An improved quaternion-based Kalman filter for real-time tracking of rigid body orientation,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 2, 2003, pp. 1074–1079. [19] X. Yun, C. Aparicio, E. Bachmann, and R. McGhee, “Implementation and experimental results of a quaternion-based Kalman filter for human body motion tracking,” in Proceedings of the IEEE International Conference on Robotics and Automation, 2005, pp. 317–322. [20] Y. Bar-Shalom, K. Chang, and H. Blom, “Tracking a maneuvering target using input estimation versus the interacting multiple model algorithm,” IEEE Transactions on Aerospace and Electronic Systems, vol. 25, no. 2, pp. 296–300, 1989. [21] X. Li and Y. Bar-Shalom, “Performance prediction of the interacting multiple model algorithm,” IEEE Transactions on Aerospace and Electronic Systems, vol. 29, no. 3, pp. 755–771, July 1993. [22] Y. Bar-Shalom, X. R. Li, and T. Kirubarajan, Estimation with Applications to Tracking and Navigation: Theory, Algorithms, and Software. Wiley, New York, 2001. [23] D. Smith and S. Singh, “Approaches to multisensor data fusion in target tracking: a survey,” IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 12, pp. 1696–1710, 2006. [24] M. J. Caruso, “Applications of magnetic sensors for low cost compass systems,” in Proceedings of the IEEE Position Location Navigation Symp, 2000, pp. 177–184. [25] M. J. Caruso, “Applications of magnetoresistive sensors in navigation systems,” Honeywell Inc., Tech. Rep., 2003. [26] D. Roetenberg, H. Luinge, C. Baten, and P. Veltink, “Compensation of magnetic disturbances improves inertial and magnetic sensing of human body segment orientation,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 13, no. 3, pp. 395–405, 2005. [27] I. Duman, “Design, implementation, and testing of a real-time software system for a quaternion-based attitude estimation filter,” Master’s thesis, Naval Postgraduate School, Monterey, CA, March 1999. [28] J. Marins, X. Yun, E. Bachmann, R. McGhee, and M. Zyda, “An extended Kalman filter for quaternion-based orientation estimation using MARG sensors,” in Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 4, 2001, pp. 2003–2011. [29] A.Watt and M.Watt, Advanced Animation and Rendering Techniques, Theory and Practice. New York, New York: ACM Press, Addison-Wesley, 1998. [30] J. M. Cooke, M. J. Zyda, D. R. Pratt, and R. B. McGhee, “NPSNET: Flight simulation dynamic modeling using quaternions,” in Proceedings of Presence, vol. 1, no. 4, Fall 1992, pp. 404–420. [31] E. Pervin and J. A. Webb, Quaternions in Computer Vision and Robotics. CMU-CS-82-150, 1982. [32] J. R. Wert, Spacecraft Attitude Determination and Control. Springer, Berlin and New York, 1978. [33] F. Gulmammadov, “Analysis, modeling and compensation of bias drift in MEMS inertial sensors,” in Proceedings of 4th International Conference on Recent Advances in Space Technologies, 2009, pp. 591–596. [34] F. Golnaraghi and B. C. Kuo, Automatic Control Systems, 9th ed. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2009. [35] F. Markley and D. Mortari, “Quaternion attitude estimation using vector observations,” The Journal of the Astronautical Sciences, vol. 48, no. 2 and 3, pp. 359–380, Apr.-Sep. 2000. [36] G.Wahba, “Problem 65-1: a least squares estimate of satellite attitude,” SIAM Rev., vol. 7, no. 3, p. 409, Jul 1965. [37] M. Shuster and S. Oh, “Three-axis attitude determination from vector observations,” Journal of Guidance and Control, vol. 4, no. 1, pp. 70–77, 1981. [38] F. Markley, “Attitude determination from vector observations: a fast optimal matrix algorithm,” The Journal of the Astronautical Sciences, vol. 41, no. 2, pp. 261–280, Apr./Jun. 1993. [39] E. Bachmann, “Inertial and magnetic tracking of limb segment orientation for inserting humans into synthetic environments,” Ph.D. dissertation, Naval Postgraduate School, Monterey, California, December 2000. [40] J. Crassidis and J. Junkins, Optimal Estimation of Dynamic System. Chapman & Hall, 2004. [41] R. McGhee, “The factored quaternion method for orientation estimation from measured earth gravity and magnetic field,” MOVES Inst., Naval Postgraduate School, Monterey, CA, Tech. Rep., 2004. [42] S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory, Vol. 1. New Jersey: Prentice Hall, 1993. [43] C.-S. Hsieh, “General two-stage extended Kalman filters,” IEEE Transactions on Automatic Control, vol. 48, no. 2, pp. 289–293, 2003. [44] M. Ahmadi, A. Khayatian, and P. Karimaghaee, “Orientation estimation by error-state extended Kalman filter in quaternion vector space,” in Proceedings of SICE Annual Conference, 2007, pp. 60–67. [45] M. Psiaki, “Attitude determination filtering via extended quaternion estimation,” Journal of Guidance, Control, and Dynamics, vol. 23, no. 2, pp. 206–214, 2000. [46] J. Kuipers, Quaternions and Rotation Sequences: A Primer with Applications to Orbits, Aerospace and Virtual Reality. Princeton University Press, August 2002. [47] E. Mazor, A. Averbuch, Y. Bar-Shalom, and J. Dayan, “Interacting multiple model methods in target tracking: a survey,” IEEE Transactions on Aerospace and Electronic Systems, vol. 34, no. 1, pp. 103–123, 1998. [48] T. Kirubarajan and Y. Bar-Shalom, “Kalman filter versus IMM estimator: when do we need the latter?” IEEE Transactions on Aerospace and Electronic Systems, vol. 39, no. 4, pp. 1452–1457, 2003. |
電子全文 Fulltext |
本電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。 論文使用權限 Thesis access permission:校內校外均不公開 not available 開放時間 Available: 校內 Campus:永不公開 not available 校外 Off-campus:永不公開 not available 您的 IP(校外) 位址是 174.129.59.198 論文開放下載的時間是 校外不公開 Your IP address is 174.129.59.198 This thesis will be available to you on Indicate off-campus access is not available. |
紙本論文 Printed copies |
紙本論文的公開資訊在102學年度以後相對較為完整。如果需要查詢101學年度以前的紙本論文公開資訊,請聯繫圖資處紙本論文服務櫃台。如有不便之處敬請見諒。 開放時間 available 已公開 available |
QR Code |