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系統識別號 U0026-1708201712004000
論文名稱(中文) 伺服端路徑衰減參數相依訊號紋比對結合三邊定位法之藍芽室內定位系統
論文名稱(英文) BLE-based Indoor Positioning and Tracking System using Server-Side n-dependent Fingerprinting associated with Trilateration
校院名稱 成功大學
系所名稱(中) 電腦與通信工程研究所
系所名稱(英) Institute of Computer & Communication
學年度 105
學期 2
出版年 106
研究生(中文) 張瑞元
研究生(英文) Jui-Yuan Chang
電子信箱 ray.chang925@gmail.com
學號 Q36041054
學位類別 碩士
語文別 中文
論文頁數 61頁
口試委員 指導教授-陳中和
口試委員-蘇文鈺
口試委員-陳培殷
口試委員-蕭勝夫
口試委員-黃穎聰
中文關鍵字 低功率藍芽  室內定位追蹤  訊號紋比對法  三邊定位法  Kalman Filter 
英文關鍵字 BLE  Indoor Positioning and Tracking  Fingerprinting  Trilateration  Kalman Filter 
學科別分類
中文摘要 近幾年由於室內適地性服務LBS(Location-Based Service)的需求,帶動許多室內定位追蹤(Indoor Positioning and Tracking)相關的學術研究,隨著藍芽技術的發展,低功率藍芽技術BLE(Bluetooth Low Energy)的推出,其省電架構、低掃描週期、低晶片成本與在市場高普及度等優勢,使得室內定位,有了更具整體效益的選擇。目前有相當多的研究,基於以BLE beacons作為訊號的發送端,並以目標的行動裝置作為訊號接收端,並執行定位追蹤分析所需的運算,但此種模式,對目標端的裝置相當耗電,不利於執行長時間的定位與追蹤。
本論文提出改以目標端的裝置作為訊號發送源,只執行BLE訊號之發送,來節省目標端電量的消耗,另設置接收器(Agents)負責訊號的接收並傳送至中心伺服器,伺服器根據訊號紋比對法(Fingerprinting)的步驟,於離線階段,將收到的目標RSS向量進行濾波並根據規劃的實體參考點(reference point)計算出相對應的路徑衰減參數向量(n vector)後,結合兩者進而建立路徑衰減參數相依訊號紋對照表(the n-dependent fingerprint table);於上線階段,目標RSS向量進行樣式比對後,對產出的訊號紋,提取路徑衰減參數向量並轉換成距離向量後,投入三邊定位法(Trilateration)計算出目標的位置。本架構將定位追蹤所需運算量,轉在伺服器端處理,以利實現長時間的定位與追蹤之目的。在定位精準度方面,Kalman filter被妥善地應用於系統的前、中、後分別擔任第一階RSS值的濾波、第二階距離平順化與第三階座標位置平順化的任務,使系統能達到對靜態目標定位平均反應時間為0.645秒與平均定位誤差1.21m;對動態目標進行追蹤時,平均反應時間0.653秒和行進軌跡的平均差異度為0.56m。
英文摘要 In recent years, many researches about indoor localization are emerging due to requirements of Location-Based Services (LBS). Since Bluetooth Low Energy (BLE) was announced in 2010, its competitive advantages, such like power efficient architecture, low cost chipset, short scan duration and wide adoption in devices have brought a potential opportunity for indoor positioning and tracking.
Most of existing researches employ BLE beacons to be signal emitters; the target’s device is designated to collect signals and execute most of computations for positioning and tracking. Apparently, it’s not viable for a long-term use purpose due to the significant power consumptions on the target’s device.
In this thesis, we turn to designate the target’s device to emit BLE signals only in order to save power consumptions on target’s device, the receivers (called agents) are additionally set to collect and deliver RSS signals received from the target to a central server where fingerprinting technique is used to form the n-dependent fingerprint table during off-line phase. When the system moves to on-line phase, the system locates target’s position by trilateration fed with a distance vector converted from target’s RSS vector and n vector extracted from the result of pattern-matching. Kalman filters are well utilized in the beginning, the middle, and the end of the system to do signal filtering and smoothing, that makes the system efficient enough to execute the tracking on a dynamic target and achieve 1.21m average localization error for a static target.
論文目次 摘要 I
SUMMARY II
INTRODUCTION II
SYSTEM DESIGN AND IMPLEMENTATION III
EXPERIMENTAL RESULT V
CONCLUSION VIII
誌謝 IX
目錄 X
表目錄 XIII
圖目錄 XIV
第1章 序論 1
1.1 研究動機 1
1.2 研究貢獻 2
1.3 論文架構 2
第2章 背景知識與相關研究 3
2.1 接收訊號強度RSS 3
2.2 傳播路徑損耗模型 3
2.3 卡爾曼濾波器 5
2.4 K-近鄰演算法 8
2.5 三邊定位法 9
2.6 文獻探討 10
2.6.1 Inverse Fingerprinting : Server Side Indoor Localization with Bluetooth Low Energy[6] 10
2.6.2 Location Fingerprinting With Bluetooth Low Energy Beacons[18] 13
2.6.3 Indoor Positioning in Bluetooth Networks using Fingerprinting and Lateration approach[19] 18
第3章 系統架構之設計與實現 22
3.1 RSS濾波與聚合程序 23
3.1.1 RSS 訊號濾波 24
3.1.2 RSS 訊號聚合 24
3.2 離線訓練階段 25
3.2.1 訊號紋採集路徑 25
3.2.2 R-N轉換 (RSS向量至N向量轉換) 26
3.2.3 路徑衰減參數相依之訊號紋對照表 27
3.3 上線定位追蹤階段 28
3.3.1 樣式比對與N向量之提取 29
3.3.2 R-D轉換 (RSS向量至距離向量轉換) 30
3.3.3 三邊定位法 31
3.4 網頁顯示介面 32
3.4.1 目標座標位置平順化 32
3.4.2 目標座標位置顯示 33
第4章 系統平台架構與驗證方法 34
4.1 實驗平台建置 34
4.1.1 中心伺服器設置 34
4.1.2 接收器設置 34
4.1.3 目標裝置設置 35
4.2 實驗方法配置 35
4.2.1 實驗場域設置 36
4.2.2 離線階段訊號紋採集方法設置 36
4.2.3 上線定位追蹤方式設置 37
第5章 實驗結果與數據分析 39
5.1 Kalman Filter於系統內之貢獻 39
5.1.1 第一階段 – RSS訊號之濾波 39
5.1.2 第二階段 – 距離向量平順化處理 40
5.1.3 第三階段 – 目標座標位置平順化處理 44
5.2 靜態目標定位誤差評估 47
5.3 動態目標追蹤效能評估 53
5.3.1 定位反應時間 53
5.3.2 定位軌跡相似度 55
第6章 結論與未來展望 57
參考文獻 58
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