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dc.contributor.authorLyu, Jia-Shangen_US
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dc.description.abstract室內無線區域網路(WLAN)定位系統的基本原理,在於使用定位時接收到的訊號強度(Received signal strengths ,RSS),與訓練時定位點收到的訊號強度做比較,找出訊號強度最相似的做為判斷位置的依據。然而,由於同一位置上量測到的訊號強度,很容易因為環境的改變,像是有人走動、空氣濕度等等因素而使得訊號強度產生變化進而導致定位上的誤差。 本研究提出一個依照環境變化自動產生虛擬訓練資料的無線網路定位模型,在本模型中,AP除了會蒐集行動裝置的訊號強度,也會蒐集其它AP的訊號強度。在實際定位時,系統會比較離線階段(Off-line phase)與上線定位階段(On-line phase)AP間彼此量測到的訊號強度強弱,作為調整大尺度傳播模型(Large-Scale Propagation Model)中路徑衰退係數(Path loss exponent)的依據,然後藉由調整過後的路徑衰退係數配合大尺度傳播模型去計算出各個定位點的虛擬訊號資料,使得產生的虛擬訓練資料更符合當下的環境。 藉由本研究所提出的方法,我們改善了在環境變化的情形下無線網路定位系統的準確度,並且因為可自行產生虛擬訓練資料,降低了訓練階段人工蒐集訓練資料的工作負擔,對於無線網路定位系統的推廣,或是位置感知系統的發展將有相當大的幫助。zh_TW
dc.description.abstractThe principle of indoor positioning system using Wi-Fi is to compare the received signal strength (RSS) in testing phase with the RSS collected in training phase and then predict the position based on the RSS model built in the training phase. However, the RSS is so sensitive that it's easily influenced by the environmental changes. In this thesis, we proposed a Wi-Fi localization model which could adapt the environmental changes and generate the virtual training data. In this localization model, the APs not only record the RSS of mobile devices but also record the RSS between APs. While doing localization, the system will compare the RSS in training phase against the RSS between APs in testing phase. According to the results, the system will adjust the path loss exponent in the large-scale propagation model and then produce the virtual training data which can best describe the environmental changes. By using the proposed model, we can improve the localization accuracy and save the time spent on collecting training data. Furthermore, the system uses APs as the RSS collectors, which makes the system much easier to be deployed.en_US
dc.description.tableofcontents第一章 緒論 1 1.1 研究動機與背景 1 1.2 論文目的與貢獻 2 1.3 論文架構 3 第二章 相關研究 4 2.1 無線定位系統 4 2.1.1 GPS 6 2.1.2 紅外線 6 2.1.3 超音波 7 2.1.4 RFID 7 2.1.5 Sensor 8 2.1.6 Wi-Fi 8 第三章 定位系統架構與方法 13 3.1 定位系統架構 13 3.2 定位系統流程 15 3.3 定位模組設計 17 第四章 系統實作與結果 24 4.1 實驗環境 24 4.2 實作方法 27 4.3 使用RSS計算歐氏距離定位準確度實驗結果 29 4.3.1 與訓練階段相同環境之定位結果 31 4.3.2 晴天室內有人之定位結果 32 4.3.3 雨天室內有人之定位結果 33 4.3.4 有人走動或站在被定位裝置旁之定位結果 34 4.3.5 是否調整平均準確度比較 35 4.4 使用RSS之差計算歐氏距離之定位結果 36 4.4.1 與訓練階段相同環境之定位結果 36 4.4.2 晴天室內有人之定位結果 37 4.4.3 雨天室內有人之定位結果 37 4.4.4 有人走動或站在被定位裝置旁之定位結果 38 4.4.5 是否使用RSS差平均準確度比較 39 4.5 經過調整資料比例 39 4.6 調整前後之正確與錯誤比較 40 第五章 結論與未來展望 43 5.1 結論 43 5.2 未來展望 44 參考文獻 45zh_TW
dc.subjectAccess Pointzh_TW
dc.titleAn Adaptive Indoor Wireless LAN Locating Model Using Virtual Training Data Generated by Environmental Changesen_US
dc.typeThesis and Dissertationzh_TW
item.openairetypeThesis and Dissertation-
item.fulltextno fulltext-
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