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研究生： 邱靖倢
Ching-Chieh Chiu
論文名稱： 利用iBeacon與Wi-Fi混合式無線技術設計與實現室內定位系統
Implementation and Analysis of Hybrid Wireless Indoor Positioning with iBeacon and Wi-Fi
指導教授： 呂政修
Jenq-Shiou Leu
口試委員: 周承復
Cheng-Fu Chou
阮聖彰
Shanq-Jang Ruan
陳維美
Wei-Mei Chen
吳晉賢
Chin-Hsien Wu
學位類別： 碩士
Master
系所名稱： 電資學院 - 電子工程系
Department of Electronic and Computer Engineering
論文出版年： 2016
畢業學年度： 104
語文別： 中文
論文頁數： 78
中文關鍵詞： Wi-Fi 、iBeacon 、室內定位 、訊號紋路特徵比對 、最近鄰居法 、混合式系統 、接收訊號強度 、適地性服務
外文關鍵詞： Wi-Fi, iBeacon, Indoor Positioning, Fingerprinting, K-Nearest Neighbors (KNN), Hybrid System, Mobile Application, Received Signal Strength Indication (RSSI)
相關次數： 點閱：548  下載：6 
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    • 定位是適地性服務(Location-Based Service，LBS)裡最重要的步驟。而由於最常用來定位的全球定位系統(Global Positioning System，GPS)所發出的訊號會被牆壁建築物所阻礙，使得在室內的使用者沒辦法透過GPS得知他們所在的位置。大多數的室內定位(Indoor Positioning)系統都依據從室內無線發射裝置所接收到的接收訊號強度(Received Signal Strength Indicator，RSSI)來當作定位的參考。藉由整合室內定位的資訊到現今智慧型手機的應用程式裡，可以更容易得到室內位置的資訊。
    　　在這篇論文中，我們提出一個新的室內定位設計方式藉由接收訊號強度指紋(RSSI Fingerprint)，接收從周圍環境中的Wi-Fi存取點(Wi-Fi APs)所發出來的無線訊號強度，同時間也透過iBeacon的所發出來的訊號強度做為輔助，藉由這種混合式的無線技術，讓智慧型手機(Smartphone)的使用者可以得到他們的室內位置。有了周圍所收集到的Wi-Fi 接收訊號強度的幫助，以及限制並區分不同方位與方向所接收到的訊號強度，並且緩和訊號的跳動，我們所提出來的設計可以克服在室內訊號極度不穩定的問題和提升定位的準確度。同時，所提出來的接收訊號強度指紋和訊號足跡比對方法可以加快定位的速度。在實驗結果裡證明我們所提出來的設計在室內環境裡可以達到一定程度的定位準確度，並且勝過其它的方法。

    Abstract —Nowadays, the demand for indoor position and navigation with location based services (LBSs) is increasing. Many applications on smartphones exploit different techniques and inputs for positioning. However, the indoor environment is really complex so that the accuracy of indoor positioning is affected by severe signal interference. Most of the wireless indoor positioning systems have relied on received signal strength indicators(RSSIs) from wireless radio emitting devices. In this paper, we propose a hybrid system assisted by the RSSI fingerprint, utilizing iBeacon to assist Wi-Fi indoor positioning. In this hybrid system, Wi-Fi APs can divide the space into different sections, and iBeacon can accurately locate where the user is in an indoor environment. We aim to provide a more accurate, effective hybrid wireless indoor positioning system using Wi-Fi and iBeacon radio signals. The experiment results show that our proposed hybrid system can achieve a certain level of accuracy in the environments. According to the analysis, the hybrid system is proven to be an effective solution to indoor positioning.

    論文摘要 III ABSTRACT IV 誌謝 V 目錄 VI 第1章 緒論 1 1.1 研究背景與動機 1 1.2 研究目的 4 1.3 章節提要 5 第2章 定位相關技術 6 2.1 定位原理探討 6 2.1.1 收訊時間測量法(Time of Arrival，TOA) 7 2.1.2 收訊時間差測量法(Time Difference of Arrival, TDOA) 9 2.1.3 接收訊號角度測量法(Angle of Arrival，AOA) 11 2.1.4 三角技術測量法(Triangulation Technique) 12 2.1.5 接收訊號強度(Received Signal Strength Indicator，RSSI) 13 2.1.6 定位方法的比較 14 2.2 定位感測技術 15 2.2.1 全球衛星定位系統(Global Positioning System，GPS) 16 2.2.2 輔助式全球定位系統(Assistant Global Positioning System，AGPS) 17 2.2.3 紅外線定位系統(Infrared Radiation，IR) 18 2.2.4 超音波定位系統(Ultra Sound) 19 2.2.5 ZigBee定位系統 20 2.2.6 無線射頻辨識系統(Radio Frequency Identification，RFID) 21 2.2.7 Wi-Fi 定位技術 22 2.2.8 藍芽定位系統(Bluetooth) 23 2.2.9 定位感測技術的比較 24 2.3 無線電波傳遞特性 25 2.3.1 反射與折射(Reflection and Refraction) 25 2.3.2 繞射(Diffraction) 26 2.3.3 散射(Scattering) 26 2.3.4 多重路徑效應(Multipath Effect) 27 第3章 室內定位系統的設計 28 3.1 設計步驟 28 3.2 系統架構 29 3.3 混合式系統架構 32 3.3.1 以iBeacon藍芽訊號做為室內定位 32 3.3.2 以Wi-Fi AP訊號做為室內定位 33 3.3.3 以Wi-Fi 與BLE訊號做為室內定位 34 3.4 建構接收訊號強度指紋資料庫 36 3.4.1 iBeacon 36 3.4.2 訊號採集 38 3.4.3 訊號濾波處理 39 3.5 定位模型建立 41 3.5.1 K-最近鄰居法(KNN，K-Nearest Neighbor) 41 3.5.2 支持向量機學習方法(SVM，Support Vector Machine) 43 3.5.3 類神經網絡(Artificial Neural Network，ANN) 44 第4章 實驗測試與評估結果 46 4.1 室內定位系統裝置介紹 46 4.1.1 智慧型行動裝置 46 4.1.2 iBeacon藍芽發射器 47 4.2 軟體工具 48 4.2.1 智慧型行動裝置定位程式開發平台 48 4.2.2 單機模擬程式開發平台 49 4.3 實驗測試環境場地 50 4.3.1 電資學院EE705-6實驗場域之環境建置 51 4.3.2 電資學院EE7F實驗場域之環境建置 52 4.3.3 手機應用程式 53 4.4 實驗測試 54 4.4.1 濾波器的比較 54 4.4.2 採集時間的比較 57 4.4.3 K-NN之K值的比較 58 4.4.4 不同定位訓練模型的比較 59 4.4.5 不同方格之準確度的比較 60 4.4.6 Beacon訊號之Radio Map 62 4.5 實驗評估結果 63 第5章 結論 65 參考文獻 66

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