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研究生:陳嶽東
研究生(外文):Yueh-Tung Chen
論文名稱:以訊號強度為基礎利用高斯混合模型之IEEE802.11無線區域網路定位演算法
論文名稱(外文):Signal Strength – Based Positioning Algorithm Using Gaussian Mixture Model for IEEE 802.11 WLAN
指導教授:鄭憲宗鄭憲宗引用關係
指導教授(外文):Sheng-Tzong Cheng
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊工程學系碩博士班
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:2005
畢業學年度:93
語文別:英文
論文頁數:54
中文關鍵詞:基地台IEEE 802.11多重路徑高斯混合模型EM演算法無線區域網路全球衛星定位系統
外文關鍵詞:Wireless Location Area Network (WLAN)multi-pathGaussian Mixture Model (GMM)Global Positioning System (GPS)Access Point (AP)Radio Frequency (RF)EM algorithmReceived Signal Strength (RSS)IEEE 802.11Location-Based Service (LBS)
相關次數:
  • 被引用被引用:16
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  • 下載下載:94
  • 收藏至我的研究室書目清單書目收藏:1
最近十年來,能夠根據移動用戶預先配置主動提供位置相關資訊以定位為基礎之服務(Location-based Service, LBS))快速的成長。使得定位系統(Location System)長久被認為新興行動應用主要部分。全球衛星定位系統(Global Positioning System)目前實際運作的室外環境位置感知系統。然而全球衛星定位系統不能夠在室內環境成功的運作且需要額外的硬體支援。因為採取大量現行存在的無線網路且在不增加的硬體條件,提出定位系統能夠在運作在室外環境並且在室內環境。而IEEE 802.11為目前當下最流行的無線區域網路。IEEE 802.11無線區域網路廣泛的部署以提供定位為基礎之服務。此外,許多研究專心致力於IEEE 802.11無線區域網路之精準室內定位採用隨者不同位置使得從不同基地台(Access Point, AP)接收的訊號強度也不同的事實。由於無線電訊號會被雜訊(noise),干擾(interference),多重路徑(multi-path),與隨意的在環境中移動等影響,我們提出利用高斯混合模型(Gaussian Mixture Model, GMM)透過EM演算法(EM Algorithm)去解決多重路徑的影響。我們也實驗去證明我們所提出的以訊號強度為基礎之定位演算法有效解決多重路徑的影響提升定位精確度。
In the last decade, there has been a rapid growth in the area of Location-Based Service (LBS). LBS can actively push location-dependent information to mobile users according to their predefined profiles. Location system has been identified as an important component of emerging mobile applications for a long time. The Global Positioning System (GPS) is currently the actual system for location sensing in outdoor wireless environments. However, GPS does not work well in indoor environments and requires dedicated hardware. Because of adopting the large number of existing wireless networks and requires no additional hardware, the proposed system is able to operate in outdoor environments as well as in indoor environments. The most popular Wireless Location Area Network (WLAN) technology nowadays based on the IEEE 802.11. WLAN has been widely deployed for LBS. In addition, most researches have focused on precise indoor location for IEEE 802.11 WLAN which adopt the received signal strength (RSS) that varies with location from different Access Points (APs). Because of the Radio Frequency (RF) signals are affected by noise, interference, multi-path effect and random movement in the environment, we introduce Gaussian Mixture Model (GMM) approximated signal propagation via EM algorithm to solve multi-path effect. The experiment demonstrates the effectiveness of proposed signal strength based Positioning algorithm.
摘要.....i
Abstract.....ii
致謝.....iii
Table of Content.....iv
List of Figure.....v
List of Table.....vi
Chapter 1 Introduction.....1
Chapter 2 Background and Related Work.....3
  2.1 Taxonomy of Location Techniques.....3
  2.2 IEEE 802.11 Standard and Positioning Techniques for IEEE 802.11.....7
  2.3 Finite Mixture Model.....9
  2.4 Likelihood Estimation for Mixture Models via EM Algorithm.....13
  2.5 Network Driver Interface Specification.....18
Chapter 3 Experimental Platform.....22
  3.1 WLAN Infrastructure and Hardware.....23
  3.2 Software Experimental Framework.....26
Chapter 4 Approximated Probability Distribution Modeling.....28
  4.1 Signal Propagation (Noise Characteristics) in IEEE 802.11 WLAN.....28
  4.2 Mixture Model with Normal (Gaussian) Distribution.....31
  4.3 Gaussian Mixture Models Via EM Algorithm.....32
  4.4 K-Largest Probability Point in Gaussian Mixture Model Technique.....33
Chapter 5 Evaluation.....38
  5.1 Parameter of GMM.....38
  5.2 Density of Training Point (Distance between nearest training point d).....44
  5.3 Impact Number of Access Point.....46
  5.4 Impact of Accuracy.....47
Chapter 6 Conclusions.....48
Chapter 7 Reference.....49
Reference Books

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