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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 張瑞益 | |
| dc.contributor.author | Jia-Shian Lin | en |
| dc.contributor.author | 林佳憲 | zh_TW |
| dc.date.accessioned | 2021-06-15T01:12:56Z | - |
| dc.date.available | 2011-08-14 | |
| dc.date.copyright | 2009-08-14 | |
| dc.date.issued | 2009 | |
| dc.date.submitted | 2009-07-29 | |
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Ghogho, “A Joint TOA/AOA Constrained Minimization Method for Locating Wireless devices in Non-Line-of-Sight Environment,” IEEE International Conference on Vehicular Technology, pp.496–500, Oct. 2007. [28] E.E.L. Lau and W.Y. Chung, “Enhanced RSSI-Based Real-Time User Location Tracking System for Indoor and Outdoor Environments,” IEEE International Conference on Convergence Information Technology, pp.1213–1218, Nov. 2007. [29] C.D. Wann and H.C. Chin, “Hybrid TOA/RSSI Wireless Location with Unconstrained Nonlinear Optimization for Indoor UWB Channels,” IEEE International Conference on Wireless Communications and Networking, pp. 3940 – 3945, Mar. 2007. [30] N. Bulusu, J. Heidemann, and D. Estrin, “GPS-less Low Cost Outdoor Localization for Very Small Devices,” IEEE International Journal of Personal Communications, volume 7, issue 5, pp.28–34, Oct. 2000. [31] J. Blumenthal, R.Grossmann, F. Golatowski, and D. Timmermann, “Weighted Centroid Localization in Zigbee-based Sensor Networks,” IEEE International Symposium on Intelligent Signal Processing, pp.1–6, Oct. 2007. [32] I.H. Witten, E. Frank, “Data Mining: Practical Machine Learning Tools and Techniques,” Second Edition, Morgan Kaufmann, Jun. 2005. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/42383 | - |
| dc.description.abstract | 對於許多應用服務來說，若能提供物品位置的定位資訊，將能提供很高的附加價值。因此近幾年定位方法受到廣泛的討論，國內外學者紛紛針對不同的室內及室外環境需求，提出對應的方法來改善定位的準確度。在傳統定位方法中，常需要透過一些額外的設備來輔助計算位置，例如：GPS[21][22][23]、Infrared[24]和Ultrasound[25]；但是節點上額外的輔助設備會增加電力消耗及系統建置成本。本論文利用節點本身所具有的RF晶片進行室內定位，以常用於室內定位的樣式比對演算法為基礎，改善其訓練時間長、計算量大與定位準確度不夠等缺點。我們實際使用本計畫所開發的無線感測器來進行驗證，實驗結果顯示，我們所提出的方法與傳統樣式比對方法相比，不但可有效地提高最多達41%的室內定位精確度，在同樣室內定位精確度下亦能有效減少訓練pattern的數量。 | zh_TW |
| dc.description.abstract | Tracing the location of a specific item has induced abundant applications with the development of wireless sensor networks. In past years, different location algorithms have been proposed for indoor or outdoor environments. Some of these use additional equipments, such as GPS[21][22][23], Infrared[24] and Ultrasound[25]. However, these equipments will increase the power consumption and system cost. In this paper, we propose a Self-learning Indoor Locating Algorithm for Wireless Sensor Networks and use only the embedded RF chip to improve the PM (Pattern Matching) algorithm. We made some improvements on saving training time, computation, and higher the positioning precision. Experiments show that our proposed algorithm not only lower training time but also higher positioning precision in 41%. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T01:12:56Z (GMT). No. of bitstreams: 1 ntu-98-R96525069-1.pdf: 2241992 bytes, checksum: 41781ee14569181e4bd7a85c8aeff68a (MD5) Previous issue date: 2009 | en |
| dc.description.tableofcontents | 口試委員會審定書 I
誌謝 II 摘要 III ABSTRACT IV 第一章 緒論 1 1.1 研究背景 1 1.2 研究動機與目的 1 1.3 架構 2 第二章 相關研究 3 2.1 依測量方法分類 3 2.1.1 AOA 3 2.1.2 TOA 4 2.1.3 TDOA 4 2.1.4 RSSI 4 2.2 依演算法分類 5 2.2.1 Centroid algorithm 5 2.2.2 Triangulation Method 6 2.2.3 Pattern Matching/Fingerprinting 6 2.3 適合室內的PATTERN MATCHING定位演算法 7 2.3.1 Regression 8 2.3.2 Nearest Neighbor Signal Strength 9 2.3.3 Statistical 9 2.3.4 Neural Networks 10 2.3.5 Support Vector Regression 11 第三章 提出演算法之系統流程 13 3.1 訓練階段 14 3.2 定位階段 18 第四章 方法實做 19 4.1 訓練階段 19 4.1.1 Filter 19 4.1.2 Predictor 22 4.1.3 Selector 23 4.2 定位階段 24 第五章 實驗結果 25 5.1 測試環境 25 5.2 演算法參數的選擇 27 5.3 訓練時間分析 28 5.4 定位精準度分析 29 第六章 結論 37 | |
| dc.language.iso | zh-TW | |
| dc.subject | 室內定位演算法 | zh_TW |
| dc.subject | 無線感測網路 | zh_TW |
| dc.subject | 樣式比對法 | zh_TW |
| dc.subject | 訊號特徵紋 | zh_TW |
| dc.subject | Indoor Locating Algorithm | en |
| dc.subject | Wireless sensor networks (WSN) | en |
| dc.subject | Fingerprinting | en |
| dc.subject | Pattern Matching | en |
| dc.title | 具有自我學習機制之無線感測網路室內定位演算法 | zh_TW |
| dc.title | Self-learning Indoor Locating Algorithm for Wireless Sensor Networks | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 97-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 丁肇隆,黃乾綱,王家輝,林正偉 | |
| dc.subject.keyword | 無線感測網路,室內定位演算法,樣式比對法,訊號特徵紋, | zh_TW |
| dc.subject.keyword | Wireless sensor networks (WSN),Indoor Locating Algorithm,Pattern Matching,Fingerprinting, | en |
| dc.relation.page | 42 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2009-07-29 | |
| dc.contributor.author-college | 工學院 | zh_TW |
| dc.contributor.author-dept | 工程科學及海洋工程學研究所 | zh_TW |
| 顯示於系所單位： | 工程科學及海洋工程學系 | |
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| ntu-98-1.pdf 未授權公開取用 | 2.19 MB | Adobe PDF |
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