在全球定位系統(GPS)日漸普及的大環境下，定位與導航也逐漸成為生活中不可或缺的一部份，但是目前大部分的定位系統只侷限在戶外環境下使用，且大部分都是運用在交通工具上，對於行人定位方面反而較少，甚至在大樓林立或是地下道等環境下，GPS也常常會有脫鎖的情況發生，為此，本論文期望能研發出一套行人方位推估系統(Pedestrian Dead Reckoning System，PDRS)，專門用於行人的定位上，此系統除了可以協助GPS解決脫鎖的問題外，更可以用在室內定位上。在一些分秒必爭危險環境，例如火災現場，假如救難人員自己發生意外或是發現需要緊急搶救的患者，而該救難人員身上正好有配置行人方位推估系統的話，此時其他的救難人員就可以在第一時間內趕去支援，提升災難發生時的存活率。
在系統硬體部分，主要是以嵌入式系統(Embedded System)為主，嵌入式系統具有低功耗和方便攜帶兩種特性，剛好符合本論文行人定位的取向，因此在平台方面，本論文採用TI 的OMAP35x EVM，再搭配上感測器：慣性量測單元(Inertial Measurement Unit，IMU)和電子羅盤(Compass)，提供行人方位推估系統所需要的量測資訊，並且將感測器量測到的資訊，透過無線網路傳輸模組傳送給遠端的OMAP35x EVM，進行行人方位推估演算法的演算，最後達到即時定位(Simultaneous location)與遠端監控(Remote monitoring)兩大功能。
在系統軟體部分，主要是以Linux為本論文的作業系統，再搭配上本論文使用的開發軟體Qt，建構出行人方位推估系統的軟體部分。
在系統演算法部分，本論文設計的行人方位推估演算法，主要是由步伐偵測、步距估測以及方位推估這三個演算法結合而成，再搭配誤差修正以及座標轉換等處理，建構出行人方位推估演算法。

Positioning and navigation systems play an important role in our daily life, but now most of positioning systems were confined in outdoor environments, most of which were used on transportation. Therefore, the goal of this thesis is to develop a Pedestrian Dead Reckoning System (PDRS), which can not only be used to solve a problem of GPS out-of-lock, but also be used in the field of indoor positioning. In dangerous environments, such as the scene of a fire, when the rescue personnel have an accident on himself or discover a wounded who need to be salvaged, if the rescue personnel who has configured the PDRS, then the other rescue personnel can assist them immediately.
In the part of hardware system, we used embedded system to be the primary part of the entire system, the embedded system has the characters of low power consumption and portability. Therefore, we chose the TI OMAP35x EVM platform to be our primary system of PDRS. In order to get the information of pedestrian, we also need the Inertial Measurement Unit (IMU) and Compass to provide the information of acceleration and heading for PDRS. To achieve the function of remote monitoring, we used wireless transmission module to send data of sensors to OMAP35x EVM. Finally, the most important function that we must accomplish in this thesis is to use OMAP35x EVM to build a real-time PDRS.
In the part of software system, we use Linux OS and Qt SDK to build the software system of PDRS in this thesis.
In the part of algorithm, we use step detection, step length estimation and dead reckoning method to construct the algorithm of PDRS in this thesis.

論文審定書 i
誌謝 ii
中文摘要 iii
英文摘要 iv
第 一 章 緒論 1
1.1 研究動機 1
1.2 文獻回顧 2
1.3 主要貢獻 9
1.4 論文架構 10
第 二 章 作業系統與軟體開發套件 11
2.1 Linux OS 11
2.2 Embedded OS 12
2.3 Qt SDK 13
第 三 章 系統硬體解析 15
3.1 OMAP35x EVM 15
3.1.1 ARM架構下之程式開發 18
3.2 慣性量測單元 20
3.2.1 資料格式 22
3.3 電子羅盤 22
3.3.1 資料格式 24
3.4無線網路傳輸模組 24
3.4.1 AT+i_Programmer 26
3.4.2 模組傳輸機制設計 26
3.5 行動電源 27
3.6 無線路由器 28
第 四 章 行人方位推估演算法 30
4.1 步態分析 30
4.2 步伐偵測演算法 31
4.3 大地座標轉換 37
4.4 積分誤差修正演算法 41
4.5 步距估測演算法 44
4.6 方位推估演算法 47
第 五 章 系統設計 49
5.1 行人方位推估系統 49
5.2 系統程式設計 54
第 六 章 實驗結果 61
6.1 室內定位 61
6.2 室外定位 65
6.3 上下樓梯 70
6.4 本論文與其它論文的比較 73
第 七 章 結論與未來展望 79
7.1 結論 79
7.2 未來展望 79
參考文獻 81
附錄 83
附錄A OMAP35x EVM基本驅動 83
A.1 嵌入Embedded OS 83
A.2 驅動觸控螢幕與Embedded Qt 85
附錄B 積分誤差修正演算法實驗數據 90
附錄C 步距估測演算法實驗數據 102
附錄D 步距估測比較實驗數據 114

[1] C. Fischer and H. Gellersen, ”Location and Navigation Support for Emergency Responders A Survey,” IEEE Pervasive Computing, pp. 38 – 47, 2010.
[2] R. Feliz, E. Zalama and J. Gomez, “Pedestrian tracking using inertial sensors,” Journal of Physical Agents, Vol. 3 No. 1, pp. 35-43, 2009.
[3] C. Zhou, J. Downey, D. Stancil and T. Mukherjee, “A Low-Power Shoe-Embedded Radar for Aiding Pedestrian Inertial Navigation,” IEEE Microwave Theory and Techniques, pp. 2521 – 2528, 2010.
[4] C. Zhou, J. Downey, J. Chio, D. Stacil, J. Paramesh and T. Mukherjee, “A Shoe-Embedded RF Sensor for Motion Detection,” IEEE Microwave and Wireless Components Letters, pp. 169 – 171, 2011.
[5] J. W. Kim, H. J. Jang, D. H. Hwang and C. Park, “A Step Stride and Heading Determination for the Pedestrian Navigation System,” Journal of Global Positioning Systems, Vol. 3 No. 1-2, pp. 273 – 279, 2004.
[6] L. Fang, P. J. Antsaklis, L. A. Montestruque, M. B. McMickell, M. Lemmon, Y. Sun, H. Fang, I. Koutroulis, M. Haenggi, M. Xie and X. Xie, “Design of a Wireless Assisted Pedestrian Dead Reckoning System-The NavMote Experience,” IEEE Instrumentation and Measurement, pp. 2342 – 2358, 2005.
[7] E. Foxlin, “Pedestrian Tracking with Shoe-Mounted Inertial Sensors,” IEEE Computer Graphics and Applications, pp. 38 – 46, 2005.
[8] O. Bebek, M. A. Suster, S. Rajgopal, M. J. Fu, X. Huang, M. C. lu, D. J. Young, M. Mehregany, A. J. van den Bogert and C. H. Mastrangelo, “Personal Navigation via High-Resolution Gait-Corrected Inertial Measurement Units,” IEEE Instrumentation and Measurement, pp. 3018 – 3027, 2010.
[9] 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, pp. 1349 – 1359, 2010.
[10] WIKIPEDIA – Linux: http://zh.wikipedia.org/wiki/Linux.
[11] WIKIPEDIA – Qt: http://zh.wikipedia.org/wiki/Qt.
[12] WIKIPEDIA – 編譯器: http://zh.wikipedia.org/wiki/%E7%BC%96%E8%AF%91%E5%99%A8.
[13] OMAP3530_DVSDK_GettingStartedGuide.
[14] Code Sourcery: http://www.codesourcery.com/gnu_toolchains/arm/releases.
[15] MTi and MTx User Manual.
[16] WIKIPEDIA - Hayes command set: http://zh.wikipedia.org/wiki/%E6%B5%B7%E6%96%AF%E5%91%BD%E4%BB%A4%E9%9B%86.
[17] A. R. Jimenez, F. Seco, C. Prieto and J. Guevara, “A Comparison of Pedestrian Dead-Reckoning Algorithms using a Low-Cost MEMS IMU,” IEEE conference on Intelligent Signal Processing, pp. 37 – 42, 2009.
[18] Frank’s資訊法學統合部落格: http://finalfrank.pixnet.net/blog/post/24522864.
[19] 賴紹偉，“自主式運動型機器人於十字路口之設計與應用”，碩士論文，國立中山大學機械與機電工程學系，2010。
[20] WIKIPEDIA – IEEE 754: http://zh.wikipedia.org/wiki/IEEE_754.
[21] WIKIPEDIA – Building Qt: http://processors.wiki.ti.com/index.php/Building_Qt.