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研究生:簡盧德
研究生(外文):Chien Lu,Te
論文名稱:利用動態訊號資料庫以減少測量數之無線網路定位系統
論文名稱(外文):Reducing Calibration Effort for WLAN Locating System with Dynamic Radio Map
指導教授:蔡子傑蔡子傑引用關係
指導教授(外文):Tsai,Tzu-Chief
學位類別:碩士
校院名稱:國立政治大學
系所名稱:資訊科學學系
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:2007
畢業學年度:96
語文別:英文
論文頁數:59
中文關鍵詞:定位無線網路訊號強度機器學習資料庫
外文關鍵詞:locatingWLANsignal strengthlearningradio map
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隨著無線網路的興起,許多相關的研究議題也應運而生,利用無線網路(WLAN)對於使用者位置的判斷與追蹤就是其中相當熱門的一塊。經過近幾年的發展,室內WLAN定位誤差的進步空間已達到極限,其原因主要出在無線訊號傳播的物理性質所產生的侷限。然而,大部分擁有良好精準度的定位系統是建立在不切實際的人力成本上,故我們將著眼點放在如何減少收集大量訊號所耗費的人力,同時保持不錯的精準度。取得AP位置所消耗的人力資源也是我們考慮的一環。

因此,我們提出一套新的定位系統:首先建立少數的資料點,再透過推測基地台位置和插入機制來完成訊號資料庫的初步建置。然後在定位的同時收集使用者接收到連續的訊號強度,透過隱馬可夫鏈建立的模型,再配合其他演算法來更新訊號資料庫。實驗結果顯示,相較於其他兩個定位系統,我們的系統能夠減少最多的人力建置資源,並且達到有競爭力的定位精準度。除此之外,我們也分析了系統在使用舊的資料庫或是不同的實驗環境下,能夠展現怎樣的定位結果。
Following the raise of Wireless LAN networks, there are a lot of relative research issues in today’s life. Tracking and locating mobile users in RF-based WLAN (IEEE 802.11) is a very important issue in location-based applications area. The error distances of indoor WLAN locating was decreased to approximately 1.5 meter in recent years. However, the improvement in accuracy was limited due to the nature of radio propagation. Many researches which contain precise accuracy were based on an impractical effort of collecting too much signal data which we usually called “calibration” in this area. So this thesis focuses on how to reduce the calibration efforts without losing too much accuracy. Confirming the allocation of access points is another kind of calibration effort we concerned.

As a consequence, we proposed a new locating system: first we calibrated few points and utilized inferring AP’s position and interpolation to complete radio map. During location estimation phase, radio map could be updated dynamically using learning mechanism modeled by HMM and other algorithms. In the experimental results, we proved our system maintained a comparable accuracy under reducing much calibration effort than other two locating systems. Besides, we analyzed the performance of our system with elder radio map and in two different experimental environments.
CHAPTER 1 Introduction 1
1.1. Background 1
1.1.1. RF-based Indoors Location Technology 1
1.1.2. Wireless Channel Propagation 3
1.1.3. Wireless Prediction and Tracking Technology 5
1.1.4. Learning Technology 6
1.2. Motivation 9
1.3. Organization 9
CHAPTER 2 Related Work 11
2.1. RADAR [1,2] 12
2.1.1. Empirical Method 13
2.1.2. Radio Propagation Method 14
2.2. Reducing Calibration Effort for WLAN Location System Using Segment Technique with Autocorrelation [24] 15
CHAPTER 3 Locating System Analysis 17
3.1. Calibration 18
3.2. Guess AP’s Position 20
3.3. Interpolation 23
3.4. Location Estimation Models 24
3.4.1. Locating Model 25
3.4.2. Tracking Model 27
CHAPTER 4 Radio Map Refreshing 28
4.1. Learning Mechanism 28
4.1.1. HMM 29
4.1.2. The Baum-Welch Algorithm 31
4.1.3. The Viterbi Algorithm 34
4.2. Radio Map Updating 35
4.2.1. Erase Interpolation Points 36
4.2.2. Write New Learning Points 37
4.2.3. Re-guess APs’ Positions 39
4.2.4. Re-interpolate Radio Map 40
CHAPTER 5 Experimental Evaluation 42
5.1. Experimental Setup 42
5.2. Experimental Results 44
5.2.1. Results of Reducing Calibration Effort 44
5.2.2. Results of Locating with Elder Calibration Points 47
5.2.3. Result of Locating in Different Environments 49
CHAPTER 6 Conclusions and Future Works 54
6.1. Conclusions 54
6.2. Future Works 55
References 56
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