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研究生:陳宥佐
研究生(外文):Yu-Tso Chen
論文名稱:在無線感測網路中以接收訊號強度為基準之混合式室內定位法
論文名稱(外文):A Hybrid RSSI-based Indoor Localization Scheme in Wireless Sensor Networks
指導教授:張燕光
指導教授(外文):Yeim-Kuan Chang
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:2009
畢業學年度:97
語文別:英文
論文頁數:56
中文關鍵詞:室內定位ZigBee 模組無線感測網路數位居家照護
外文關鍵詞:CC2431wireless sensor networksZigBee modulesindoor localization
相關次數:
  • 被引用被引用:1
  • 點閱點閱:236
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
近年來家庭自動化程式推陳出新,若是要提供好的自動化服務則需要精確找出使用者位置的能力,室內定位也是最近熱門的研究主題對於在室內環境中自動地提供數位居家照護服務給使用者。然而室內定位由於在室內無法使用常見的全球位置測定系統 (Global Positioning System, GPS),最近幾年無線感測網路 (Wireless Sensor Networks)的蓬勃發展,加上無線感測網路的性質正好適合實作室內定位,延伸出根據無線感測網路的物理特性進而實作室內定位的眾多相關討論議題。在本論文中,我們討論接收訊號強度 (Received Signal Strength Indication,RSSI),並且提出能讓使用者處於室內環境接近某個參考節點時能正確判斷的CTA(Closer Tracking Algorithm)混和風格方法,在我們所提出的CTA 方法藉由事先定義的門檻值設計成切換ACA (Approximately Closer Approach)與RTT (Real-time Tracking)兩種模式,藉由切換兩種模式來達到適應更多定位情形的發生,此外也提出移動參考節點來減少沒有達到我們事先定義的門檻值內的情形以增加定位的準確性。我們實驗採用IEEE 802.15.4 協定的CC2431 晶片ZigBee 模組實作我們的方法以及比較其它方法(如Fingerprinting、Real-Time Tracking 方法),從實驗結果我們可以驗證我們提出的CTA 方法在不同情況下挑選了適合的模式並且達到了增加定位的準確性以及證實定位結果有非常高的可信度,此外我們透過改善參考節點的擺設以及移動參考節點搭配上我們所提出的方法讓我們的定位準確度更加準確。實驗結果表示我們的方法準確率可以達到誤差值在一公尺以內,以及在距離參考節點一公尺範圍內達到90%的精確性。
For the various applications in home automation, the service system requires to precisely estimate user’s locations by certain sensors. It is considered as a challenge to automatically serve a mobile user in an indoor environment. However, indoor localization cannot be carried out effectively by the well-know Global Positioning System (GPS). In recent years, Wireless Sensor Networks (WSNs) are thus popularly used to locate a mobile object in an indoor environment. Some physical features are widely discussed to solve indoor localization in WSNs. In this paper, we inquired about the RSSI-based solutions for indoor localization, and proposed a new hybrid-styled Closer Tracking Algorithm (CTA) to locate a mobile user in an indoor environment. Under the proposed CTA, a mode-changed operation was designed to automatically switch the Approximately Closer Approach (ACA) and the Real-time Tracking (RTT) methods according to the pre-defined thresholds, which we had tuned. At the same time, we designed the movable reference nodes to reduce the uncovered ranges of the RTT part for increasing the accuracy. The proposed CTA was evaluated by using ZigBee CC2431 modules. In the experimental results, the CTA can properly select an adaptive mode to improve the localization accuracy with high confidence. Furthermore, the accuracy can be improved by the deployment and movement of the reference nodes. The results showed that the proposed CTA can accurately determine the position with error distance less than 1 meter. At the same time, the CTA has at least 90% precision when the distance is less than one meter.
Chapter 1 Introduction 1
1.1 Motivation 1
1.2 Overview of the Thesis 1
Chapter 2 Background 3
2.1 What is Indoor Localization 3
2.2 Classification of Indoor Localization Systems 7
2.2.1 Classification based on Wireless Signals 7
2.2.1.1 Ultrasound 7
2.2.1.2 Infrared (IR) 8
2.2.1.3 Radio frequency (RF) 9
2.2.2 Classification based on measurement principle 9
2.2.2.1 Angle of Arrival (AOA) 10
2.2.2.2 Time of Arrival (TOA) 11
2.2.2.3 Time Difference of Arrival (TDOA) 12
2.2.2.4 Received Signal Strength Indication (RSSI) 13
Chapter 3 Related Work 14
3.1 Fingerprinting 14
3.2 Real-Time Tracking 15
3.3 Fingerprinting Comparing with Real-Time Tracking 17
Chapter 4 Proposed Algorithm 21
4.1 Definitions 21
4.2 Closer Tracking Algorithm 22
4.2.1 [Step1 Build Neighbor List] 24
4.2.3 [Step3 Adapt Assistant Position] 25
4.2.4 [Step4 Approximately Closer Approach] 25
4.2.5 [Step5 Tracking Path to Blind Node] 25
Chapter 5 Improve the Proposed CTA 31
5.1 Motivation 31
5.2 Definitions 32
5.3 Improved Closer Tracking Algorithm 32
5.3.1 [Step1 Find Nearest Node] 33
5.3.2 [Step2 Determine Mode] 33
5.3.3 [Step3 Improved Approximately Closer Approach] 33
5.4 Move a Reference Node 37
5.4.1 Limit the directions of moving reference nodes 37
5.4.2 Unlimited directions of moving the reference nodes 38
Chapter 6 Implementation and Experiment 40
6.1 Experimental Setup 42
6.1.1 Findings 42
6.2 Experimental Results 44
Chapter 7 Conclusion 53
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