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研究生:黃曉祈
研究生(外文):WONG, HIU-KAY DANIEL
論文名稱:基於FCM訊號歸屬度與深度學習之多樓層室內定位機制
論文名稱(外文):A Multi-Floor Indoor Positioning Mechanism Based on FCM Signal Membership Value and Deep-Learning
指導教授:曾俊元
指導教授(外文):TSENG, CHIN-YANG
口試委員:莊東穎曹偉駿黃俊穎吳育松曾俊元
口試委員(外文):JUANG, TONG-YINGTSAUR, WOEI-JIUNNHUANG, CHUN-YINGWU, YU-SUNGTSENG, CHIN-YANG
口試日期:2020-07-28
學位類別:碩士
校院名稱:國立臺北大學
系所名稱:資訊工程學系
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:2020
畢業學年度:108
語文別:中文
論文頁數:44
中文關鍵詞:接收信號強度指標深度學習經緯度模糊C平均
外文關鍵詞:RSSIFuzzy C-MeansDeep-LearningLatitude and Longitude
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隨著科技的急速發展,高樓大廈林立,室內環境增加和環境複雜度提高,進而提高了室內定位的需求,並且由於建築物每層環境的差異,Received Signal Strength Indication (RSSI)值會有不同分佈,而且鮮少有能配合各層差異作出準確的定位機制。本論文利用Fuzzy C-Means(FCM)找出不同樓層所有無線存取點(Wireless Access Point,WAP)的中心點,然後把收集到的RSSI值計算出相對應樓層的歸屬度,讓樓層的差異性能透過歸屬度表示出來,也成為之後定位機制的輸入。定位機制透過自動編碼器(Autoencoder,AE)加上深度神經網路(Deep Neural Network,DNN), 使用了UJIndoorLoc資料集訓練,輸入歸屬度,辨別樓層之間造成的數值差異,最後計算出近似的經緯度。在用資料集中的測試集測試後,結果顯示在WAPs中心點的中心點範圍廣及其群聚程度低的情況下本論文提出的方法有得到最好的效果,減低大約25%的平均誤差、大約20%的最小誤差和大約28%的最大誤差。
Due to the rapid development of technology plus urbanization, indoor environment’s area and complexity are increased and therefore requirement of indoor positioning is raising. Moreover, Received Signal Strength Indication (RSSI) has different distribution in different floor because of the floors’ difference which not many tools consider the problem. Fuzzy C-Means(FCM) is used to find center point(CP) of Wireless Access Point(WAP) from different floor. Thus, calculating the Membership Value(MV) from RSSI to it can show the floors’ difference clearly and it will be the input of the neural network which consist of Autoencoder(AE) and Deep Neural Network(DNN). Latitude and longitude are the output of neural network which the input is UJIndoorLoc dataset can learn floors’ difference. As a result, when CP range of WAPs’ CP is wide and clustering is low, around 25% average error, 20% minimum error and 28% maximum error are decreased.
謝 辭 I
中文論文提要 Ⅱ
英文論文提要 III
目 錄 Ⅳ
圖 目 錄 Ⅴ
表 目 錄 Ⅵ
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 3
1.3 研究目的 5
1.4 論文架構 5
第二章 文獻探討 6
2.1 深度學習定位方法 6
2.2 現有針對多樓層的室內定位方法 7
2.3 FCM應用 8
第三章 FCM訊號歸屬度與深度學習之多樓層室內定位機制 9
3.1 系統架構 9
3.2 訊號歸屬度 12
3.3 深度學習定位機制模型 14
3.4 算法總結與演算法 17
第四章 實驗結果與分析 18
4.1 實驗環境 18
4.2 正規化 19
4.3 中心點比較 21
4.4 深度學習模型比較 26
4.5 小結 36
第五章 結論 37
參考文獻 38



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