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研究生:謝政偉
研究生(外文):Cheng-Way Hsieh
論文名稱:新穎的無線網路定位系統
論文名稱(外文):A Novel Wireless Network Location System
指導教授:林宗男林宗男引用關係
口試委員:蔡子傑廖婉君陳俊良
口試日期:2011-07-28
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:電信工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:英文
論文頁數:72
中文關鍵詞:指紋辨識無線網域選擇特徵支持向量機遞迴移除
外文關鍵詞:fingerprintWLANselectionfeatureSVMRFE
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這篇論文提出了一種在室內無線網路的環境之下,一種布建無線基地台的新方法。不同於過去傳統的方式,大多著重於範圍的最大化,這篇論文中,以室內無線網路定位系統的觀點出發,來研究如何布建室內無線基地台。我們提出了一種連接室內基地台以及定位效能的架構。在我們提出的演算法之中,使用了一種向後特徵選擇概念為基礎的特徵選擇法,在此稱之為群體識別標準。這個標準可以考慮到不同基地台之間的依賴性,並且利用支持向量機中的風險函數,做為這個標準的量化準則。此外,我們也提出使用一種本地搜尋的方法來解決一些此方法在複雜度以及消耗時間上的問題,這種本地搜尋的方法,利用支持向量機的風險函數,在每一次的遞迴之中,刪去最差的特徵,因此在這個方法之下,我們可以再合理的時間內找到一組次佳的無線基地台來提供室內定位之用。我們的定位系統,建立在實際的無線網路環境之中。從各種不同的情境實驗之下,其結果皆可以證明此論文所提出的方法,相較於以往的無線基地台選擇方法,都具有較優異的表現。

Chinese abstract I
English abstract II
List of Figures iii
List of Tables vii
1 Introduction 1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
2 BACKGROUND of INDOOR LOCATION SYSTEM 2
2.1 Wireless Location Estimation . . . . . . . . . . . . . . . . . . . . 5
2.2 RSS Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2.3 Location Fingerprinting System . . . . . . . . . . . . . . . . . . . 9
2.3.1 Offline Stage . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.3.2 Online Stage . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3 MACHINE LEARNING MECHANISM 13
3.1 Maximum Likelyhood . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.2 Kernel Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.3 Support Vector Machine . . . . . . . . . . . . . . . . . . . . . . . 17
3.3.1 Linearly separable case . . . . . . . . . . . . . . . . . . . . 17
3.3.2 Linearly non-separable case . . . . . . . . . . . . . . . . . 20
3.3.3 Nonlinear extension by kernel trick . . . . . . . . . . . . . 22
CONTENTS
3.3.4 Extension to multiclass classification problem . . . . . . . 24
4 ACCESS POINTS DEPLOYMENT 27
4.1 Importance Quantification for AP selection . . . . . . . . . . . . . 29
4.2 Feature Selection Method . . . . . . . . . . . . . . . . . . . . . . 30
4.3 A Novel Criterion . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
4.4 Recursive Feature Elimination . . . . . . . . . . . . . . . . . . . . 34
5 ANALYSIS 41
5.1 Analysis of RSS Measurement Space . . . . . . . . . . . . . . . . 41
5.2 Analysis of Redundant Model . . . . . . . . . . . . . . . . . . . . 44
6 EXPERIMENTAL RESULTS AND ANALYSIS 49
6.1 Performance Evaluation . . . . . . . . . . . . . . . . . . . . . . . 50
6.2 Evaluation in different experimental environment . . . . . . . . . 51
6.3 Performance under different mobile device . . . . . . . . . . . . . 53
6.4 Alternative fingerprinting system-Kernel based system . . . . . . 54
6.5 Alternative fingerprinting system-Maximum likely hood . . . . . . 56
7 CONCLUSION 63
Bibliography 67

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