臺灣博碩士論文加值系統

近年來，社會大眾越來越慣於使用區域性資訊服務(Location-based service)與相關應用服務，隨著微機電技術的日趨發達以及行動通訊裝置的多元化發展，對於室內空間定位服務的需要也應運而生。因此，成功大學通訊與導航實驗室開發出一套名為「即時室內定位系統」(Real Time Indoor Positioning System, RTIPS)的室內定位系統測試平台，使用虛擬實境概念並且整合了自行開發的地理資訊系統，是一套能夠提供使用者即時性位置感知服務的室內定位系統。然而，此系統的開發背景環境空間為航太系系館的一間教室，這對於將來的商業或醫療應用需要將開發的背景空間擴大。另一方面，由於定位演算法的需要，隨著空間擴大，對於定位所需要的資料庫蒐集，也會耗費更多的時間與人力成本，為使本室內定位系統能夠保持原有定位表現與即時性，本論文在資料庫擴建的工作上做更詳盡的研究。

因此，本論文為了讓定位系統能更廣泛的被應用與更高的可用性，將重心放在資料庫演算法的擴建上，利用兩種空間推估演算法來擴建並校正資料庫，兩種演算法分別為克利金(Kriging)演算法與反距加權(Inverse Distance Weighted)演算法。除此之外，在克利金法的推估演算過程中，以往在計算特殊參數時，必須針對不同的感測器去一一計算其參數，為了能更有效率地決定這些參數，本文建構一套完整的程序，而且對於不同的室內環境，能夠更快速根據實驗所蒐集的資料庫，準確的估算出參數並建立適當的理論模型去近似實驗空間訊號接收情形，有別於以往利用試誤法來決定參數的方式，節省了人力以及時間成本。另外，為了能提供不同室內空間的使用者使用此定位系統，我們也更進一步研究以找出在不同的實驗空間範圍中最適當的感測器數目。最後，為使此室內定位系統更為完善，本論文將使用者的定位結果誤差界限(Position Error Bound)定義並計算出來，對於室內定位系統的使用者的生命安全，提供了更大的保障與更完整的室內定位服務。

Nowadays, there is a great need for real time indoor positioning systems of mobile users, since the well-developed MEMS technology and there are many kinds of mobile communications devices. It seems that Location Based Service is getting popular to people. Therefore, a low cost and low power consumption Real Time Indoor Positioning System (RTIPS) that is integrated with a self-developed indoor Geographic Information System (GIS) has been developed by Hsu, et al of National Cheng Kung University (NCKU). In order to make RTIPS more flexible to applications, this work tries to implement RTIPS to a larger indoor environment. Once the application location is larger, the challenge we meet is the positioning database calibration because the costs related to time and labor for calibrating a wide area are much higher than those for a smaller site. The purpose of this thesis is to achieve a more available system for an indoor positioning system; thus, this thesis extends the study on the database calibration algorithms of a fingerprint positioning algorithm.

In the database calibration stage, the collected signal quality might be affected if the positioning space is geometrically complicated. Thus, the signal transmission paths will be more complicated as well. As a result, we place an emphasis on utilizing two spatial interpolation methods including: 1) the Inverse Distance Weighting (IDW) method; 2) the Kriging method, that are used to yield a denser database from the raw data measurements. Additionally, another purpose of this thesis is to establish a procedure to determine the parameters of the theoretical models since most researches mentioned the parameters regarding as the “sill” and “range” estimation of a semi-variogram that usually depend on a trial and error approach, which consumes a great deal of time. We also conduct a study to investigate the optimal sensor numbers for different indoor environments for users. Finally, in order to make this system more complete, we determine the confidence bound of the positioning result by using the estimation variance provided by the Kriging method through the interpolation process. Moreover, we modify the estimation variance to define and calculate the confidence bound for indoor positioning, to provide users with a more safe indoor positioning service.

ABSTRACT III
ACKNOWLEDGEMENTS V
List of Content VI
List of Tables VIII
List of Figures IX
Chapter 1 INTRODUCTION 1
1.1 Motivation 1
1.2 Previous Work 2
1.3 Objectives 4
1.4 Thesis Organization 5
Chapter 2 SPATIAL INTERPOLATION METHODS 6
2.1 Introduction 6
2.2 The Inverse Distance Weighting Algorithm 8
2.3 The Kringing Algorithm 10
2.4 Summary 18
Chapter 3 INDOOR POSITIONING ALGORITHM 19
3.1 Fingerprint Method 19
3.2 The K-Weighted Nearest Neighbors Algorithm 21
3.3 Error Bound 23
3.4 Summary 23
Chapter 4 EXPERIMENT RESULTS AND ANALYSIS 25
4.1 Experiment Setup 25
4.2 Database Calibration 30
4.3 Positioning Results 45
4.4 Further Work 51
4.5 Summary 58
Chapter 5 CONCLUSIONS AND FUTURE WORK 59
5.1 Conclusions 59
5.2 Future Work 60
References 61

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