臺灣博碩士論文加值系統

最近, 利用Wi-Fi 做室內定位的技術變得更加引人關注, 因為利用廣泛被佈置的Wi-Fi系統可以減少系統在硬體建置的負荷, 此外, 有先前的研究驗證使用Wi-Fi在正交分頻調變(Orthogonal frequency-division multiplexing, OFDM) 運作下的精密評估資訊, 也就是通道狀態訊息(Channel State Information, CSI), 用以作為位置指紋比傳統以接收訊號強度(RSSI)更具代表性。這篇論文中, 將會分享關於利用學校已建置的Wi-Fi系統去實作一個以通道狀態訊息為基礎的定位系統。
我們的系統包含了「指紋資料庫」和「位置評估系統」兩個部分。因為在我們的測試環境中可以明顯觀察到多個不同的通道狀態訊群，所以我們利用K-means 演算法分群，並保留每個探勘點的多個指紋在「指紋資料庫」中。
因為精密的通道狀態訊息是以高維度的向量呈現，所以我們利用一種統計模型R平方數 (R-square value) 來做指紋比對。除了單一通道狀態訊息封包的比對測試方法之外，文中也提供一個可以達到更高定位精準度的多通道狀態訊息封包的比對測試方法。同時為了避免受到易受位置影響的通道狀態訊息而導致的誤判, 亦提供了一個可行的權重投票估計機制。最後我們顯示系統的評估結果, 也可以看到我們的系統表現比傳統利用接收訊號強度實作的系統突出。

Nowadays, Wi-Fi-based indoor localization techniques have become attractive, because widely deployed Wi-Fi system could reduce the overhead of infrastructure. Moreover, some prior works argue that Wi-Fi OFDM-based fine-grained estimation data, Channel State Information (CSI), is more representative than traditional RSSI as location fingerprints. In this paper, we shared the experience of utilizing the school built-in Wi-Fi system to build a CSI-based localization system.
Our system includes “Fingerprint Database” and “Localization System.” Due to multiple obvious CSI clusters could be observed in our testbed, we utilize K-means algorithm to retain multiple fingerprints for each survey point in our “Fingerprint Database”.
Because fine-grained CSI are high-dimension vectors, a statistical module (i.e., R-square value) is proposed for fingerprint comparison. Not only Single-CSI comparison, but also a Multiple-CSI comparison testing method is also proposed, which reaches higher accuracy. To reduce the location misjudgment caused by location sensitive CSI, a feasible weighted voting estimation process is also proposed. Finally, we evaluate our system in our testbed and show our system outperforms traditional RSSI-based localization system.

口試委員審定書 1
誌謝 2
摘要 3
ABSTRACT 5
CONTENTS 7
LIST OF FIGURES 11
LIST OF TABLES 14
CHAPTER 1 INTRODUCTION 15
CHAPTER 2 RELATED WORK 19
CHAPTER 3 CHANNEL STATE INFORMATION 25
3.1 Preliminaries 25
3.1.1 OFDM Technology 25
3.1.2 Frequency-Selective Fading 26
3.1.3 Channel State Information Grouping 27
3.2 CSI Observation 28
3.2.1 CSI Vector 28
3.2.2 R-Square Value 29
3.2.3 Stability Experiment 30
CHAPTER 4 FINGERPRINTING DATABASE 34
4.1 Fingerprint Database Generation 34
4.1.1 Cluster Strategy Decision 35
4.1.2 Fingerprint Database 39
CHAPTER 5 IMPLEMENTATION 41
5.1 Channel State Information Collection 41
5.1.1 Hardware Setup 41
5.1.2 Software Setup 42
5.2 Infrastructure 43
5.3 Localization Algorithm 44
5.3.1 Clustering & Data Processing Block 45
5.3.2 Fingerprint Comparison Block 46
5.3.3 Weighted Voting Estimation 49
CHAPTER 6 EVALUATION 51
6.1 Experimental Scenario 51
6.1.1 Experiment Environment 52
6.1.2 Experiment Methods 54
6.1.3 Evaluation Matrix 55
6.2 Performance Evaluation 55
6.2.1 Self-Testing 55
6.2.2 Daily Traces Localization Evaluation 56
6.2.3 Weighted Voting Estimation Evaluation 61
6.3 RSSI-based Localization System Comparison 62
6.3.1 RSSI-based Localization Mechanism 62
6.3.2 Performance Comparison 63
6.4 Comparison between our system and FIFS 64
6.4.1 FIFS System vs. our system 64
6.4.2 FIFS System Evaluation 65
CHAPTER 7 CONCLUSION & DISCUSSION 67
REFERENCE 70

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