無線感測網路之定位追蹤演算法設計與實作

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：25

、訪客IP：3.129.39.85

姓名

王經憲(Ching-Hsien Wang) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

無線感測網路之定位追蹤演算法設計與實作
(Design and Implementation of Localization and Tracking in Wireless Sensor Network)

相關論文

★ 具輸出級誤差消除機制之三位階三角積分D類放大器設計	★ 應用於無線感測網路之多模式低複雜度收發機設計
★ 用於數位D類放大器的高效能三角積分調變器設計	★ 交換電容式三角積分D類放大器電路設計
★ 適用於平行處理及排程技術的無衝突定址法演算法之快速傅立葉轉換處理器設計	★ 適用於IEEE 802.11n之4×4多輸入多輸出偵測器設計
★ 應用於無線通訊系統之同質性可組態記憶體式快速傅立葉處理器	★ 3GPP LTE正交分頻多工存取下行傳輸之接收端細胞搜尋與同步的設計與實現
★ 應用於3GPP-LTE下行多天線接收系統高速行駛下之通道追蹤與等化	★ 適用於正交分頻多工系統多輸入多輸出訊號偵測之高吞吐量QR分解設計
★ 應用於室內極高速傳輸無線傳輸系統之設計與評估	★ 適用於3GPP LTE-A之渦輪解碼器硬體設計與實作
★ 下世代數位家庭之千兆級無線通訊系統	★ 協作式通訊於超寬頻通訊系統之設計
★ 適用於3GPP-LTE系統高行車速率基頻接收機之設計	★ 多使用者多輸入輸出前編碼演算法及關鍵組件設計

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

本論文提出了在無線網路裡，利用接收訊號強度量測來計算次梯度最佳化與訊號特徵法的室內定位系統。而我們也採用的目標函式是加權最小平方估計，之所以會採用這個目標函式是因為他擁有好的函式凸型性以及能夠對抗對遮蔽效應。在分散式方法裡，無線感測器利用遞迴的方式使得目標函數近似次梯度法。本論文也提出可調變的步長，我們主要是藉由次梯度值與最小的移動距離來加速收斂速度。
在追蹤定位上除了次梯度最佳化也搭配訊號特徵法，不但可以藉此得到更快速的收斂速度，也讓初始位置得到改善。一個是大步走的訊號特徵法，一個是小步走的次梯度法，倆倆互相搭配運用，稱之為高爾夫定位演算法，合併了訊號特徵法與次梯度法的優點，搭配使用。
此外，收斂分析也證明了我們所設計的可調整式步長是會收斂的。在模擬方面，我們提出的演算法比傳統的分配式定位演算法都來的精確，不論是固定位置分析或是追蹤分析，並且收斂速度也比較快。
最後，在硬體實作方面，我們利用了硬體描述語言ISE來實現高爾夫演算法，並且比較了定點數與浮點數的誤差值，以及觀察實現的電路行為是否有追蹤的動作。

摘要(英)

In this paper, we propose a subgradient optimization method for localization based on received signal strength in wireless sensor network. The objective function of weighted least-squares estimation is adopted, which shows good convexity and has immunity to shadowing effect. We also approximate the subgradient of the objective function by a recursive form so that it can be implemented in a decentralized manner within each sensing node. A variable step size is proposed to take into consideration both the subgradient and minimum adjustment to accelerate convergence.
To improve the accuracy of positioning in a large-scale sensor network, a fingerprint method is incorporated. The localization method allows a large jump in movement. So, our proposed golf localization combines the fingerprint method with subgradient optimization.
Furthermore, the convergence analysis is also given to show the feasibility of our design for the step size. From simulation results, we can see the proposed algorithm has better accuracy and convergence rate than the conventional decentralized algorithms to localize a stationary or moving target in wireless sensor network.
Finally, we are going to introduce the hardware, and we use ISE simulation to show our proposed golf algorithm, and then compare the error value between the fixed point and floated point. So, we can look out true whether the implement circuit do tracking or not.

關鍵字(中)

★ 定位
★ 分配式演算法
★ 次梯度最佳化
★ 訊號特徵演算法

關鍵字(英)

★ and Fingerprint
★ subgradient optimization
★ distributed algorithm
★ positioning

論文目次

目錄
摘要................................................I
Abstract...........................................II
目錄................................................III
表目錄...............................................VI
圖目錄...............................................VII
第1章緒論.........................................1
1.1 研究動機.........................................1
1.2 研究目的.........................................1
1.3 相關研究文獻......................................2
1.4 論文架構.........................................3
第2章無線定位系統技術與定位演算法介紹.....................4
2.1 系統環境設定......................................4
2.1.1 各個定位演算法浮點數模擬與探討........................5
2.1.2 定位演算法的模擬範圍...............................5
2.1.3 定位演算法之目標物移動方式...........................7
2.2 無線定位量測方法介紹................................9
2.2.1 到達時間定位法(Time of Arrival )[2]................9
2.2.2 到達時間差定位法(Time Difference of Arrival)[2]....10
2.2.3 接收信號角度定位法(Angle of Arrival)[2]............10
2.2.4 接收訊號強度定位法(Received Signal Strength)[2]....11
2.3 無線定位演算法....................................14
2.3.1 加權符號演算法(Weighted Sign Algorithm)的介紹[3]...15
2.3.2 插入權重演算法(WIP)[4]............................17
2.3.3 凸集合投射(Projection onto convex sets - POCS)演算法[5] ........................................................19
2.3.4 最近的局部最小值投影(Projection Onto the Nearest Local Minimum-PONLM)演算法[6]................................21
2.3.5 多重跳躍可適應反覆的定位(Multi Hop Adaptive Iterative Localization-MAIL)演算法[7]............................23
2.3.6 利用梯度下降法獲得安全的定位(gradient descent approach for secure localization)[8]...........................29
2.3.7 訊號特徵演算法(Fingerprint Localization)[9]......30
2.3.8 改良遞增式次梯度演算法(Modified Incremental Subgradient)[10]...................................................32
2.3.9 加權遞增式次梯度演算法(Weighted Incremental Subgradient)[11]...................................................33
2.3.10 線性組合演算法(Linear Combination Algorithm, LC)[12] ......................................................34
2.4 各個演算法的特性與比較............................35
第3章高爾夫定位演算法(Golf Localization)..............36
3.1 高爾夫定位方法之流程..............................36
3.2 分配式訊號特徵演算法(Distributed Fingerprint Localization).........................................39
3.3 改良訊號強度....................................42
3.4 目標函式(Objective Function)...................45
3.5 次梯度法.......................................48
3.5.1 次梯度最佳化....................................48
3.5.2 次梯度遞迴運算式與可調變步長及收斂性分析.............52
3.6 高爾夫定位演算法(Golf Algorithm )................55
3.6.1 條件限制(Judgment).............................55
3.7 執行過程......................................61
第4章高爾夫演算法模擬與探討...........................64
4.1 高爾夫演算法步伐長度參數模擬與探討..................64
4.2 各種定位演算法模擬與探討..........................65
4.2.1 靜態浮點數模擬半徑20公尺..........................65
4.2.2 動態浮點數模擬半徑20公尺..........................67
4.2.3 靜態浮點數模擬半徑90公尺..........................71
4.2.4 動態浮點數模擬半徑90公尺..........................72
第5章硬體設計與實現..........................76
5.1 設計區塊圖..........................76
5.2 距離量測模組設計..........................77
5.3 次梯度最佳化設計..........................80
5.3.1 次梯度電路設計..........................80
5.3.2 步伐長度電路設計..........................82
5.3.3 次梯度最佳化定位電路設計..........................84
5.4 訊號特徵定位系統設計..........................85
5.5 合併次梯度與訊號特徵電路設計..........................87
5.5.1 九宮格範圍電路設計..........................88
5.5.2 餘弦定律條件電路設計..........................92
5.5.3 位置更新偵測器..........................93
5.5.4 合併次梯度與訊號特徵電路設計..........................94
第6章模擬與硬體實現..........................97
6.1 硬體模擬驗證..........................97
第7章結論..........................104
參考資料..........................105

參考文獻

參考資料
[1] N. Patwari, J. N. Ash, S. Kyperountas, A.O. Hero, III; R.L. Moses, N. S. Correal, “Locating the nodes: cooperative localization in wireless sensor networks”, IEEE Signal Processing Magazine, pp. 54 – 69, July 2005.
[2] Waltenegus Dargie, Christian Poellabauer, “Fundamentals of Wireless Sensor Networks Theory and Practice”, 1st Edition, Wiley, 2010.
[3] D. S. Wu and C. L. Wang, “Decentralized cooperative positioning and tracking based on a weighted sign algorithm for wireless sensor networks(WSA)”, in Proc. IEEE Global Telecommunications Conference, (GLOBECOM) 2009. pp.1-6.
[4] C. L. Wang, Y. W. Hong, and Y. S. Dai, “A decentralized positioning method for wireless sensor networks based on weighted interpolation(WIP),” in Proc. 2007 IEEE Int. Conf. on Commun. (ICC 2007), Jun. 2007, pp. 3167-3172.
[5] A. O. Hero, III and D. Blatt, “Sensor network source localization via projection onto convex sets (POCS),” in Proc. 2005 IEEE Int. Conf. Acoust., Speech, Signal Processing, vol. 5, Mar. 2005, pp. 1065-1068.
[6] Q. Shi and C. He, “Distributed source localization via projection onto the nearest local minimum(PONLM),” in Proc. 2008 IEEE Int. Conf. Acoust., Speech, Signal Processing (ICASSP 2008), Apr. 2008, pp. 2553-2556.
[7] S. B. Kotwal, S. Verma, G. S. Tomar , R. K. Abrol, “MAIL: Multi - Hop Adaptive Iterative Localization for Wireless Sensor Networks(MAIL)”, 2009 First International Conference on Computational Intelligence, Communication Systems and Networks, July 2009, pp. 23-25.
[8] R. Garg, A. L. Varna, and M. Wu, “Gradient descent approach for secure localization in resource constrained wireless sensor networks(GD)”, Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on , March 2010, pp.14-19.
[9] N. Wei, X. Wendong, K. T. Yue and K. T. Chen, “Fingerprint-MDS based Algorithm for Indoor Wireless Localization”, in 21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, Sept. 2010, pp.26-30.
[10] C. L. Wang, D. S. Wu, and J. H. Kuan, “Decentralized positioning and tracking based on a variable step-size incremental subgradient algorithm for wireless sensor networks(MIG),” in Proc. 2008 IEEE Int. Conf. Symposium on Personal, Indoor and Mobile Radio Commun., Sep. 2008, pp.1-5.
[11] C. L. Wu and D. S. Wu, “Decentralized positioning and tracking based on a weighted incremental subgradient algorithm for wireless sensor networks(WIG)”, in Proc. IEEE 68th Vehicular Technology Conference, Sep. 2008, pp.1-5
[12] W. Y. Chen and S. L. Miller, “Distributed linear combination estimators for localization based on received signal strength in wireless networks(LC),” Annual Conference on Information Sciences and Systems (CISS), Mar. 2009, pp. 258-263.
[13] M.G. Rabbat, “Decentralized source localization and tracking(IG)”, Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP ’’04). IEEE International Conference on, May 2004, pp.17-21.
[14] C. L. Wang, D. S. Wu and F. F. Shu, “Design and Implementation of a Decentralized Positioning System for Wireless Sensor Networks”, in Proc. IEEE Wireless Communications and Networking Conference (WCNC), Apr. 2010, pp.1-6.
[15] N. Z. Shor, ”Minimization Methods for Non-differentiable Functions,” Springer-Verlag, 1985.

指導教授

蔡佩芸(Pei-Yun Tsai)

審核日期

2012-8-28

推文