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研究生:李政霖
研究生(外文):Li, Cheng-Lin
論文名稱:以減少測量數為目標之無線網路定位系統
論文名稱(外文):Reducing Calibration Effort for WLAN Location and Tracking System
指導教授:蔡子傑蔡子傑引用關係
指導教授(外文):Tsai, Tzu-Chieh
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:2006
畢業學年度:94
語文別:英文
論文頁數:62
中文關鍵詞:無線網路定位系統無線訊號傳遞模型學習模型自我相關度
外文關鍵詞:Indoor Locating SystemWireless Channel Propagation ModelLearning ModelAutocorrelation
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  • 被引用被引用:0
  • 點閱點閱:166
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內容感知的應用在今日已經變的越來越熱門,而位置資訊的可知也因此衍生出許多研究的議題。這篇論文提出了一套精準的室內無線網路系統名為Precise Indoor Location System (PILS)。大部分擁有良好定位精準度的定位系統都必須在事情花費許多的人力在收集大量的訊號上面,使得定位系統的變的不實用與需求過多的人力資源。在這篇論文裡,我們將目標放在減少在建置訊號地圖上的人力資源耗費並且保持住定位系統的精準度在一個可以接受的範圍。我們也提出了在資料收集上、訊號內插上、以及位置估計上的模型。另外我們也考慮了一連串連續訊號的相關度來提高準確度。無線網路訊號傳遞的特性也是我們研究的一部份,大小範圍的遮蔽包含在我們所研究的訊號傳遞現象裡面。最後我們提出了一套學習的模型來調整我們的訊號地圖,以改進因為測量數目的減少所造成的精準度下降。
Context-aware applications become more and more popular in today’s life. Location-aware information derives a lot of research issues. This thesis presents a precise indoor RF-based WLAN (IEEE 802.11) locating system named Precise Indoor Locating System (PILS). Most proposed location systems acquire well location estimation results but consume high level of manual efforts to collect huge amount of signal data. As a consequence, the system becomes impractical and manpower-wasted. In this thesis, we aim to reduce the manual efforts in constructing radio map and maintain high accuracy in our system. We propose the models for data calibration, interpolating, and location estimation in PILS. In the data calibration and location estimation models, we consider the autocorrelation of signal samples to enhance accuracy. Large scale and small scale fading are involved in the wireless channel propagation model. We also propose a learning model to adjust radio map for improving the accuracy down caused by calibrated data reduction.
CHAPTER 1 INTRODUCTION 1
1.1 Background 1
1.1.1 Wireless Indoors Location Technology 1
1.1.2 Wireless Channel Propagation 4
1.1.3 Wireless Prediction and Tracking Technology 6
1.1.4 Samples Correlation 7
1.1.5 Learning Technology 11
1.2 Motivation 14
1.3 Organization 14
CHAPTER 2 RELATED WORK 15
CHAPTER 3 LOCATION ESTIMATION CONCEPT 21
3.1 Segment Process 22
3.2 Using Samples Correlation in Our System 23
3.3 Reducing Calibration Location Numbers 24
3.4 Reducing Calibration Time 25
CHAPTER 4 LOCATION ESTIMATION MODELS 27
4.1 Variable Definitions 28
4.2 Probability Model 28
4.3 Interpolation Model 29
4.4 Location Deterministic Model 30
4.4.1 Weighted Triangulation Model 31
4.4.2 Time correlation-based Model 32
4.5 Tracking Model 33
CHAPTER 5 LOCATION LEARNING MODEL 35
5.1 Hidden Markov Model 35
5.2 The Baum-Welch Algorithm 38
5.3 The Viterbi Algorithm 40
CHAPTER 6 EXPERIMENTAL EVALUATION 43
6.1 Testbed 43
6.2 Data collection 45
6.3 Experimental Result 46
6.3.1 Experimental results of reducing calibration effort without autocorrelation 46
6.3.2 Experimental results of reducing calibration effort with autocorrelation 50
6.3.3 Experimental results of learning technique 54
CHAPTER 7 CONCLUSIONS & FUTURE WORKS 57
7.1 Conclusions 57
7.2 Future Works 58
REFERENCE 59
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