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研究生:蕭宇成
研究生(外文):Yu-Cheng Hsiao
論文名稱:分散式自我定位演算法在無線隨意感測網路上之研究
論文名稱(外文):Distributed Self-Localization for Wireless Ad-Hoc Sensor Networks
指導教授:溫志煜
學位類別:碩士
校院名稱:國立中興大學
系所名稱:通訊工程研究所
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:2008
畢業學年度:96
語文別:英文
論文頁數:45
中文關鍵詞:無線感測網路自我定位隨機粒子濾波器
外文關鍵詞:Wireless Sensor NetworksSelf-LocalizationParticle Filtering
相關次數:
  • 被引用被引用:1
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  • 收藏至我的研究室書目清單書目收藏:0
如何在一個無線感測網路的環境中,建立網路連線並且估測出感測器(sensor)的座標位置是一項非常重要的研究。本篇論文提出了一個無線隨意感測網路之分散式自我定位演算法,利用網路之間訊息的傳播,將接收到的資訊作處理和運算以達成定位估測。當估測的程序和通訊協定開始執行時,我們首先會先針對區域網路做初始的定位,接下來,當所有的區域感測網路群(sensor clusters)的區域座標系統(local coordinate system)完成後, 我們應用了一個精確校正系統(refinement schemes)來降低初始估測的錯誤率,使得誤差擴散(propagation error)可以有效率的被抑制。最後再利用座標系統整合的演算法來將各區域座標系統整合成一個整體的網路座標系統(single global coordinate system) 。運算過程中,只需要利用感測器彼此之間的距離資訊而不需要使用到含有GPS功能的感測器。這篇論文詳述了定位方式的建立並且探討在設計演算法所必須要做的取捨,最後我們藉由程式模擬和數值分析,來驗證這個演算法的可行性。
This thesis proposes algorithms for establishing connectivity and location estimation in wireless sensor networks. The algorithms exploit the information flow while coping with distributed signal processing and the requirements of network scalability. Once the estimation procedure and communication protocol are performed, sensor clusters can be merged to establish a single global coordinate system without GPS sensors using only distance information. In order to adjust the sensor positions, the refinement schemes are applied to reduce the estimation error such that the propagation error can be suppressed. This thesis outlines the technical foundations of the localization techniques and presents the tradeoffs in algorithm design. The feasibility of the proposed schemes is shown to be effective under certain assumptions and the analysis is supported by simulation and numerical studies.
1. Introduction 1
2. Literature Review 3
3. Distributed Anchor-Free Positioning Algorithm (DAPA) 5
3.1. Phase I: Initial Local Position Estimation 5
3.2. Phase II: Sensor Location Adjustment 11
3.3. Phase III: Relative Global Localization 15
3.3.1 The Information Flow 15
3.3.2 Relative Global Coordinate System 16
4. Simulation and Numerical Results 23
4.1. Result of The Initial Local Position Estimation 23
4.2. Comparison between Min-max and particle filter 29
4.3. Results of Gradient Algorithm 30
4.4. Results of The Metropolis-Hastings Algorithm 32
4.5. An Example of Merging Two Clusters 38
5. Conclusion 41
6. References 42
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