本論文旨在開發一整合慣性導航系統(INS)及全球定位系統(GPS)之行人導航系統及其演算法，用以精準地獲得行人於室內外之行走軌跡。此系統透過以慣性感測技術為基礎，開發一行人軌跡重建演算法於室內行人導航，並利用擴展式卡爾曼濾波器整合慣性導航系統與全球定位系統，形成一整合INS/GPS之導航系統及其行人導航演算法於室外行人導航。首先，我們整合加速度計、陀螺儀、磁力計、微控制器與藍芽無線傳輸模組形成一行人慣性導航系統；接著，我們利用行人軌跡重建演算法重建行人行走軌跡，並配合基於地圖資訊之軌跡修正演算法與高度及斜坡偵測演算法，分別進行行人二維行走軌跡與行走高度之修正。為降低慣性感測器的誤差累積，我們開發了一融合加速度、角速度與磁力訊號之應用擴展式卡爾曼濾波器於姿態估測演算法，用以降低慣性感測器訊號的雜訊及飄移在姿態估測上所造成的積分誤差。最後，由實驗結果成功地驗證此行人導航系統與其行人導航演算法之有效性。

This thesis develops an integrated inertial navigation system (INS)/global positioning system (GPS) pedestrian navigation system and its associated pedestrian navigation algorithm to record pedestrian walking trajectories in indoor and outdoor environments. We developed a pedestrian trajectory reconstruction algorithm for indoor pedestrian navigation based on inertial sensing technology, and then utilized an extended Kalman filter to develop an integrated INS/GPS pedestrian navigation system and its algorithm for outdoor pedestrian navigation. First, the pedestrian inertial navigation system consists of a triaxial accelerometer, a triaxial gyroscope, a triaxial magnetometer, a microcontroller, and a Bluetooth wireless transmission module. The pedestrian trajectory reconstruction algorithm is able to reconstruct pedestrian walking trajectories using a map matching algorithm and a height/ramp detection algorithm to correct 2D walking trajectories and height estimation, respectively. We developed the extended Kalman filter (EKF) fusing with the accelerations, angular velocities, magnetic signals to decrease cumulative errors of the inertial sensors. Finally, the experimental results have successfully validated the effectiveness of the proposed pedestrian navigation system and its associated algorithms.

[1] K. Azadeh, K. N. Plataniotis, and A. N. Venetsanopoulos, “Kernel-based positioning in wireless local area networks,” IEEE Trans. Mobile Comput., vol. 6, no. 6, pp. 689-705, 2007.
[2] P. Aggarwal, D. Thomas, L. Ojeda, and J. Borenstein, “Map matching and heuristic elimination of gyro drift for personal navigation systems in GPS denied conditions,” Measurement Science and Technology, vol. 22, pp. 025205, 2011.
[3] K. Adbulrahim, C. Hide, T. Moore, and C. Hill, “Using constraints for shoe mounted indoor pedestrian navigation,” The journal of navigation, vol. 65, pp. 15-28, 2012.
[4] W. C. Bang, W. Chang, K.H. Kang, E.S. Choi, A. Potanin, and D.Y. Kim, “Self-contained spatial input device for wearable computers,” in Proc. of the 7th International Symposium on Wearable Computers, pp. 26-34, 2005.
[5] E. Foxlin, “Pedestrian tracking with shoe-mounted inertial sensors,” IEEE Computer Graphics and Applications, vol. 25, no. 6, pp. 38-46, 2005.
[6] C. C. Foster and G. H. Elkaim, “Extension of a two-step calibration methodology to include nonorthogonal sensor axes,” IEEE Trans. Aerosp. Electron. Syst., vol. 44, no. 3, pp. 1070-1078, 2008.
[7] H. Fourati, L. Afilal, and Y. Handrich, “A nonlinear filtering approach for the attitude and dynamic body acceleration estimation based on inertial and magnetic sensors: Bio-logging application,” IEEE Sensors Journal, vol. 11, no. 1, pp. 233-244, 2011.
[8] S. Godha and G. Lachapelle, “Foot mounted inertial system for pedestrian navigation,” Measurement Science and Technology, vol. 19, no. 7, pp. 1-9, 2008.
[9] Y. Gu, A. Lo, and I. Niemegeers, “A survey of indoor positioning systems for wireless personal networks,” IEEE Communications Surveys and Tutorials, vol. 11, no. 1, pp. 13-32, 2009.
[10] S. Hong, M. H. Lee, H. Chun, S. Kwon, and J. Speyer, “Observability of error states in GPS/INS integration,” IEEE Trans. Veh. Technol., vol. 54, no. 2, pp. 731-743, 2005.
[11] C. Huang, Z. Liao and L. Zhao, "Synergism of INS and PDR in self-contained pedestrian tracking with a miniature sensor module," IEEE Sensors Journal, vol. 10, no. 8, pp. 1349-1359, 2010.
[12] A. R. Jimenez, F. Seco, F. Zampella, J. C. Prieto, and J. Guevara, “PDR with a Foot-Mounted IMU and Ramp Detection,” IEEE Sensors Journal, vol. 11, pp. 9393-9410, 2011.
[13] K. Kaemarungsi and P. Krishnamurthy, “Modeling of indoor positioning systems based on location fingerprinting,” in Proc. IEEE INFOCOM, vol. 2, pp. 1012-1022, 2004.
[14] J. Kim and H. Jun, “Vision-based location positioning using augmented reality for indoor navigation,” IEEE Trans. Consum. Electron., vol. 54, no. 3, pp. 954-962, 2008.
[15] H. Liu, H. Darabi, P. Banerjee, and J. Liu, “Survey of wireless indoor positioning techniques and systems,” IEEE Trans. Syst. Man, and Cybern. C, vol. 37, no. 6, pp. 1067-1080, 2007.
[16] R. Mautz and S. Tilch, “Survey of optical indoor positioning systems,” International Conference on Indoor Positioning and Indoor Navigation, pp. 21-23, 2011.
[17] H. Qi and J. B. Moore, “Direct Kalman filtering approach for GPS/INS integration,” IEEE Trans. Aerosp. Electron. Syst., vol. 38, no. 2, pp. 687-693, 2002.
[18] M. Ren and H. A. Karimi, “Movement pattern recognition assisted map matching for pedestrian wheelchair navigation,” The journal of navigation, vol. 65, no. 4, pp. 617-633, 2012.
[19] A. M. Sabatini, “Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing,” IEEE Trans. Biomed. Eng., vol. 53, no. 7, pp. 1346-1356, 2006.
[20] Y. S. Suh, “Attitude estimation by multiple-mode Kalman filters,” IEEE Trans. Ind. Electron., vol. 53, no. 4, pp. 1386-1389, 2006.
[21] E. H. Shin and N. El-Sheimy, “Unscented kalman filter and attitude errors of low-cost inertial navigation systems,” Navigation, vol. 54, no. 1, pp. 1-9, 2007.
[22] S. Shin and C. Park, “Adaptive step length estimation algorithm using low-cost MEMS inertial sensors,” IEEE Sensors Applications Symposium, pp. 1-5, 2007.
[23] Z. F. Syed, P. Aggarwal, C. Goodall, X. Niu, and N. El-sheimy, “A new multi-position calibration method for MEMS inertial navigation systems,” Measurement Science and Technology, vol. 18, no. 7, pp. 1897-1907, 2007.
[24] A. M. Sabatini, “Adaptive filtering algorithms enhance the accuracy of low-cost inertial/magnetic sensing in pedestrian navigation systems,” International Journal of Computational Intelligence and Applications, vol. 7, no. 3, pp. 351-361, 2008.
[25] Z. Sun, X. Mao, W. Tian, and X. Zhang, “Activity classification and dead reckoning for pedestrian navigation with wearable sensors,” Measurement Science and Technology, vol. 20, no. 1, pp. 1-10, 2009.
[26] D. H. Titterton and J. L. Weston, Strapdown Inertial Navigation Technology, Peter Peregrinus Ltd, 2004.
[27] B. H. Wellenhof, H. Lichtenegger, and J. Collins, Global positioning system: Theory and practice, Springer, 1993.
[28] S. P. Won and F. Golnaraghi, “A triaxial accelerometer calibration method using a mathematical model,” IEEE Trans. Instrum. Meas., vol. 59, no. 8, pp. 2144-2153, 2010.
[29] S.H. P. Won, W.W. Melek, and F. Golnaraghi, “A Kalman/particle filter-based position and orientation estimation method using a position sensor/inertial measurement unit hybrid system,” IEEE Trans. Ind. Electron., vol. 57, no. 5, pp. 1787-1798, 2010.
[30] Z. Q. Zhang, X. L. Meng, and J. K. Wu, “Quaternion-based Kalman filter with vector selection for accurate orientation tracking, ” IEEE Trans. Instrum. Meas., vol. 61, no. 10, pp. 2817-2824, 2012.
[31] 莊智清、黃國興，電子導航，全華，台北，民92。
[32] 陳志正，結合最佳化理論與神經網路於GPS/INS導航系統之設計，國立臺灣海洋大學通訊與導航工程系碩士論文，2005。
[33] 鄭秉恩，混合慣性與光學感測技術之互動式手寫筆系統之研製，國立成功大學電機工程學系碩士論文，2012。