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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 江昭皚(Joe-Air Jiang) | |
dc.contributor.author | Yu-Fan Chen | en |
dc.contributor.author | 陳宇凡 | zh_TW |
dc.date.accessioned | 2021-06-16T17:33:22Z | - |
dc.date.available | 2017-08-28 | |
dc.date.copyright | 2012-08-28 | |
dc.date.issued | 2012 | |
dc.date.submitted | 2012-08-15 | |
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dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/64174 | - |
dc.description.abstract | 近年來,全球定位系統(Global Position System, GPS)為大眾所熟知的定位技術。然而,GPS定位技術的應用必須讓目標與衛星之間擁有直視距離(Line of Sight, LoS)。因此,GPS定位技術無法滿足室內定位應用上的需求。在室內定位技術的相關研究中,由於無線感測器網路(Wireless Sensor Network, WSN)技術在室內應用上,擁有體積小、方便佈建以及成本低廉等優點。同時,無線感測器節點佈建在室內時,感測器節點就像環繞在地球的衛星一樣,能夠提供目標節點進行定位計算時所需的資訊。因此,無線感測器網路技術是室內定位研究中常被使用的技術之一。
本研究利用接收訊號強度設計並實現一套分散式室內定位系統。接收訊號強度定位法擁有方便使用、無須額外硬體成本等優勢,是WSN定位技術中常被使用的一個方法。透過分散式定位法的優勢,目標節點能夠快速計算出自身的位置。無須透過閘道器或是特定節點做運算。本系統先在室內建立訊號衰減曲線,作為測量距離的依據。當目標節點藉由周遭錨節點取得足夠的距離資訊時,目標節點本身即可進行分散式定位演算法的計算。本研究考量到接收訊號強度在室內的穩定度隨距離增加而減少,因此定位演算法的部分是以三角定位法為基礎。同時,利用定位圓的伸縮與擴張的概念,修正三角定位法定位時,因為接收訊號強度受到環境影響所產生的定位誤差。 本研究以MATLAB軟體模擬此分散式室內定位系統的結果。同時,本研究克服實作上的困難,將演算法實作於無線感測器節點Octopus II上,以驗證模擬結果。經過實地實驗證實,此分散式室內定位系統的實作結果與模擬結果相符合,定位誤差分別為0.87 m與0.71 m。同時,在不同環境下的定位結果分別為0.87 m與0.67 m,證實本研究提出的定位方法能夠修正不同環境造成的定位誤差。 | zh_TW |
dc.description.abstract | In recent years, Global Position System (GPS) is the most common technology of localization. However, the applications of the GPS must include Line of Sight (LoS) between user and the satellites. For this reason, the GPS is not conformed to applications of indoor localization. Wireless Sensor Network (WSN) is one of the technologies often used in the studies of indoor localization for the advantages of small sizes, convenience of set up, and low cost. The nodes, like the satellites, can provide information for localization algorithm which set in the indoor environment.
In this study, a distributed indoor localization system based on the received signal strength indicator (RSSI) was designed and implemented. RSSI is often used in localization of WSN due to no additional hardware being needed and convenient workability. In this study, the target node can calculate its position through the distributed algorithm. In the beginning of the localization system, the RSSI is detected and the RSSI-distance curve is built between two nodes in the indoor environment. The target node in the indoor environment could start the distributed localization algorithm when it gathers enough information from neighboring anchor nodes. The distributed localization algorithm is based on triangulation location. Considering the RSSI is more unstable with increasing distance, this algorithm used the expanded local circle for reducing the located error from unstable RSSI. This study simulates the distributed indoor localization system by the MATLAB and implements this system on the Octopus II. The location error from the simulation is 0.71 m. The location errors from the implementation in various environments are 0.87 m and 0.67 m. The simulation and the implementation have similar results that confirm this system worked. The similar implementation results from different environments show that the algorithm can reduce location error from different environment. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T17:33:22Z (GMT). No. of bitstreams: 1 ntu-101-R99631021-1.pdf: 6308810 bytes, checksum: 6403eeedc33d38629a752fb70fa876db (MD5) Previous issue date: 2012 | en |
dc.description.tableofcontents | 致謝 i
中文摘要 iii Abstract iv 目錄 vi 圖目錄 ix 表目錄 xi 第一章 前言 1 1.1 研究背景 1 1.2 研究動機及目的 2 1.3 論文架構 4 第二章 無線感測器網路技術簡介 6 2.1 無線感測器網路架構 6 2.2 無線通訊協定與技術 8 2.3 ZigBee 協定 9 2.4 無線感測器節點簡介 11 2.4.1 無線感測器節點簡介 11 2.4.2 無線感測器節點—Octopus II簡介 15 2.5 無線感測器網路作業系統—TinyOS 16 第三章 定位演算法相關文獻探討 19 3.1 GPS定位系統 19 3.2 無線感測器網路定位文獻探討 20 3.2.1 質心定位法 21 3.2.2 Hop-count定位法 22 3.2.3 APIT(Approximate Point-in-Triangulation)定位法 23 3.2.4 TOA(Time of Arrival)定位法 24 3.2.5 TDOA(Time Difference of Arrival)定位法 25 3.2.6 AOA(Angle of Arrival)定位法 27 3.2.7 RSSI(Received Signal Strength Indicator)定位法 27 3.2.8 各類定位方法評估與比較 30 3.3 RSSI定位法文獻探討 31 3.3.1 訊號衰減曲線相關文獻探討 32 第四章 分散式室內定位系統實現 36 4.1 分散式室內定位系統架構 36 4.1.1 錨節點 37 4.1.2 目標節點 38 4.1.3 閘道器 39 4.2 RSSI定位演算法 40 4.2.1 接收訊號強度擷取 41 4.2.2 RSSI衰減曲線的建立 42 4.2.3 定位演算法 46 第五章 實驗結果與討論 53 5.1 室內定位系統模擬分析 53 5.1.1 訊號強度來源 53 5.1.2 定位方法性能分析 54 5.1.2.1 模擬環境設置分析 54 5.1.2.2 不規則佈放節點模擬分析 56 5.1.2.3 規則佈放節點模擬分析 57 5.2 室內定位系統實地建置 60 5.2.1 室內定位系統─無線感測器節點 61 5.2.2 室內定位系統─環境設置 61 5.2.3 於臺灣大學知武館207室之室內定位實地測試結果 64 5.2.4 於臺灣大學知武館304室之室內定位實地測試結果 66 第六章 結論與未來工作 68 參考文獻 70 | |
dc.language.iso | zh-TW | |
dc.title | 基於接收訊號強度之分散式室內定位系統 | zh_TW |
dc.title | A Distributed Indoor Localization System Based on the Received Signal Strength Indicator | en |
dc.type | Thesis | |
dc.date.schoolyear | 100-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 王永鐘,范丙林,顏炳郎 | |
dc.subject.keyword | 分散式室內定位,接收訊號強度,無線感測器網路, | zh_TW |
dc.subject.keyword | distributed indoor localization,received signal strength indicator,wireless sensor network, | en |
dc.relation.page | 78 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2012-08-15 | |
dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
dc.contributor.author-dept | 生物產業機電工程學研究所 | zh_TW |
顯示於系所單位: | 生物機電工程學系 |
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