近幾年，隨著智慧型行動裝置越來越普及，行動定位服務漸漸被大量使用在許多應用中。因為Wi-Fi的普及且不需要額外的硬體成本，使得如何利用Wi-Fi來幫助室內定位成為熱門的研究重點。雖然在received signal strength (RSS) fingerprint-based室內定位方面，已經有許多相關研究正在進行，但目前大多數的系統仍很難克服費時費力的離線訓練階段。為了改善此問題，Bahl等人[1]提出了model-based室內定位系統，利用無線訊號傳播模型來建立理論型的radio map。雖然它的精確度比其他系統低，但所需要的佈署成本卻相當低。
現在的智慧型手機整合了多種慣性感測器，例如加速度計、磁力計、陀螺儀...等，在本論文中我們利用它們來偵測使用者的移動狀態，實作出一個室內定位系統。此系統以Bahl的model-based系統為原型，並使用兩個機制來改善其精確度。第一，它使用智慧型手機上的慣性感測器來追蹤使用者的行走路徑，以幫助定位。第二，所收集到的資料將用來更新radio map以改善其準確度。
我們在Android智慧型手機上實作此系統，並評估其表現。實驗結果顯示本文的系統比原本的model-based定位系統有更好的精確度。

In recent years, mobile positioning services are increasingly used for many applications with the growing popularity of smart mobile devices. Wi-Fi indoor positioning has become a research focus due to the popularity of Wi-Fi and no-additional hardware cost. Although active researches have been conducted on received signal strength (RSS) fingerprint-based indoor position, most of the current systems hardly overcome the costly and time-consuming offline training phase. For improving this problem, Bahl et al. [1] proposed a model-based indoor positioning system, which constructed a theoretically-computed radio map by using the radio propagation model. Although the accuracy is lower than other systems, less overhead to deploy is its advantage.
On the other hand, smartphones integrate various kinds of inertial sensors such as accelerometer, magnetometer, gyroscope, etc. In this thesis, we utilize them to detect user movements and implement an indoor positioning system. This system is based on the prototype of Bahl’s model-based system and uses two mechanisms to improve its accuracy. Firstly, it tracks user's walk path with inertial sensors on the smartphone to assist positioning. Secondly, the collected data is used to update the radio map and improve its accuracy.
We implement this system on Android smartphone and evaluate the performance. The experiment of results show that our system gets better accuracy than original model-based positioning system.

論文審定書 i
致謝 ii
摘要 iii
Abstract iv
目錄 v
圖次 vii
表次 ix
第一章 導論 1
1.1 研究背景 1
1.1.1 Wi-Fi介紹 1
1.1.2 Wi-Fi定位方式 5
1.2 研究動機 6
1.3 論文架構 7
第二章 相關研究 8
2.1 室內定位演算法 8
2.1.1 抵達時間(TOA) 8
2.1.2 抵達時間差(TDOA) 9
2.1.3 抵達角度(AOA) 10
2.1.4 接收訊號強度(RSS) 10
2.2 定位相關技術 11
2.2.1 全球定位系統 11
2.2.2 紅外線定位技術 13
2.2.3 超音波定位技術 13
2.2.4 射頻識別定位技術 14
2.2.5 Wi-Fi定位技術 15
2.2.6 綜合比較 16
2.3 慣性感測器 16
第三章 系統之架構與運作 18
3.1 使用流程 18
3.2 建置階段 20
3.2.1 設定環境資訊 20
3.2.2 建立radio map 21
3.2.3 牆壁數量判斷 22
3.3 使用階段 24
3.3.1 使用流程 24
3.3.2 使用路徑定位的優點 32
3.4 紀錄階段 33
3.4.1 紀錄流程 33
3.4.2 紀錄階段的優點 34
第四章 模擬實驗與數據分析 35
4.1 環境相關設定 35
4.2 實驗方式說明 38
4.3 實驗結果 39
第五章 結論 45
參考文獻 46

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