在此研究中，我們希望可以做到以較低的成本和方便使用的方式來達到室內定位的效果。如今智慧型手機進步快速，戶外定位系統於手機上不再是新鮮的事情，但是如今的定位系統皆是使用衛星定位訊號來進行作業，在室內中使用並無法達到準確的效果，訊號會被建築物所遮蔽。因此我們希望藉此研究來做出一個使用其他方式來進行室內定位的系統。
目前各種熱門公共場所都是許多觀光客常常會去的地方，例如地下街，各百貨公司。而這些地方通常都很大，第一次去的民眾大多會逛一逛之後，搞不清楚自己目前所在的位置而迷路。如此狀況下，如果有一套室內定位系統，讓使用者隨時可以知道自己目前所在的位置是一件很方便的事情，因此我們設計了一套在室內GPS訊號不準確的室內也可以使用的室內定位系統。為了使用上方便，我們建構在現在幾乎人人皆有的智慧型手機上面。我們希望使用低成本且方便的方式來達到室內定位的效果，因此排除了在環境內架設感應器來定位的方式，而採用PDR (Pedestrian Dead Reckoning) 的方式來進行行人航位推算，即是假設起始位置已知，再以使用者走路的步數，步伐大小和方向來進行目前所在位置的推算。使用者以手動方式輸入目前所在位當作起始點位置，使用了手機內建的感應器來蒐集使用者步行的資訊。我們以加速度感應器來蒐集使用者在行走中所產生的加速度，觀察行走時所產生的加速度特徵，以此來做為判斷行走步數的依據，並且評估目前現有的步幅計算方法，取較適合本研究中使用的方法來計算步幅的大小。以陀螺儀感應器在行人轉向時所產生的數值變化，計算出使用者轉向的角度，以此方式獲得行人行走的資訊，再搭配上適當的地圖來獲得最後定位的結果，則可以達到低成本且方便的室內定位效果。

Many people get lost easily in public places if they are not familiar with the environment. It would be very helpful to have an indoor localization system. Many researches in indoor localization can be found in the literature. However, they usually need to deploy sensors in the environment. It is costly and inconvenient. In this research, we propose to develop a smartphone indoor positioning application based on accelerometer and gyroscope data. The PDR (Pedestrian Dead Reckoning) method is used to build this application. Calibration points are marked both on the floor ground and on the map of the application. The user first finds a calibration mark, stand on it and face the right direction. He/she then place the android icon (representing the user) on top of the calibration point. When he/she starts to move, the android icon also moves on the map following the real-time estimates of step length and orientation change for each step from accelerometer and gyroscope data, respectively. Preliminary results in walking distance and orientation estimation show high accuracy. The application seems promising and useful as long as a map and calibration marks are built in advance.

目錄
第一章	緒論	1
1.1	研究背景與動機	1
1.2	研究目的	3
1.3	論文組織與架構	5
第二章	文獻探討	7
2.1	Android系統	7
2.1.1	Android系統架構	7
2.1.2	Android Activity生命週期	9
2.2	手機感應器	10
2.2.1	加速度感應器	12
2.2.2	陀螺儀感應器與方向感應器	14
2.3	室內定位法	15
2.3.1	WI-FI與RFID	15
2.3.2	Pedestrian Dead Reckoning	17
2.4	計步器	18
2.5	方向判斷	20
2.6	步幅計算	21
第三章	系統流程與方法	24
3.1	系統流程	24
3.2	資料擷取	26
3.3	資料前處理	27
3.4	行走步數偵測	29
3.4.1	Sliding Window方法	29
3.4.2	Zero-Crossing方法	33
3.5	步伐大小估計	35
3.6	轉向角度計算	36
3.7	室內定位方法	39
3.7.1	起始點設定與地圖載入	39
3.7.2	整合推算定位結果	40
3.7.3	所在位置校正	41
第四章	實驗結果與分析	42
4.1	開發平台	42
4.2	系統介面	43
4.3	實驗結果分析	47
4.3.1	步數判斷	48
4.3.2	轉向角度	50
4.3.3	步伐大小	51
4.3.4	定位結果分析	53
第五章	結論與未來發展	61
5.1	結論	61
5.2	未來發展	61
參考文獻	63
附錄1 終點定位結果圖	70
附錄2 英文論文	80

圖目錄
圖 1. Android系統架構圖[10]	8
圖 2. Activity 生命週期[11]	9
圖 3. 加速度感應器原理	12
圖 4. 手機三軸座標	13
圖 5. Wi-Fi定位示意圖	16
圖 6. PDR架構圖	18
圖 7. 行走示意圖	22
圖 8. 系統流程圖	24
圖 9. Acc經過濾波比較	28
圖 10. 建立視窗示意圖	30
圖 11. 視窗向右滑動示意圖	30
圖 12. 視窗中誤判示意圖	31
圖 13. 行走步伐較大時誤判情況	33
圖 14. Zero-Crossing示意圖	34
圖 15. 行人轉向座標軸與手機座標軸關係	36
圖 16. 原地轉向測試	38
圖 17. 定位座標表示	40
圖 18. 地圖中加入校正點	41
圖 19. 功能選單	44
圖 20. 系統介面圖1	45
圖 21. 系統介面圖2	46
圖 22. 淡江大學工學大樓八樓平面圖	47
圖 23. 手持裝置晃動較大時測試結果	54
圖 24. 較注重裝置穩定下的測試結果	54
圖 25. 終點定位結果圖(長距離)	58
圖 26. 終點定位結果圖(短距離)	59
 
表格目錄
表格 1 . Android平台支援的感應器類型[13]	10
表格 2. 四種步數判斷方法錯誤率比較[35]	20
表格 3. 感應器取樣間隔時間比較[44]	27
表格 4. Sliding Window行走50步測試結果	49
表格 5. Zero-Crossing行走50步測試結果	49
表格 6. 右轉90度(-90度)測試結果	50
表格 7. 左轉90度(90度)測試結果	51
表格 8. 步行直線10公尺測試結果	52
表格 9. 終點定位結果(長距離)		56
表格 10. 終點定位結果(短距離)	57

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