Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/98245
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dc.contributor陳煥zh_TW
dc.contributor.author廖健智zh_TW
dc.contributor.authorJian-Zhi Liaoen_US
dc.contributor.other資訊科學與工程學系所zh_TW
dc.date2019zh_TW
dc.date.accessioned2019-03-22T06:29:50Z-
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dc.identifier.urihttp://hdl.handle.net/11455/98245-
dc.description.abstract近年來室外定位技術已經相當成熟,但受制於室內環境對於如GPS衛星訊號的阻絕障礙問題,興起了許多對於室內定位技術的相關研究與探討,而其中較低成本且佈建方式較為方便的技術有藍芽(Bluetooth)、Wi-Fi等,本篇論文是基於2013年由蘋果公司(Apple Inc.)所提出的iBeacon為基礎進行探討。 藍芽訊號 RSSI 值飄蕩嚴重,會造成定位效果不佳,目前大部分關於室內定位的文獻主要都是提出如何應用技術來達到和改善定位的功能,鮮少有再針對定位訊號收集密度與準確度之間關係[1]的探討,根據本研究實驗結果發現,其他條件不變之下,當定位訊號收集密度越高,定位準確度會隨之下降。 本篇論文依照建置於中興大學藝術中心之Beacon定位裝置衍生之定位不準確問題進行探討,試圖提出一套可用於改善現有室內定位準確度之演算法。 本研究透過卡爾曼濾波器(Kalman Filter,KF) 提升藍芽訊號 RSSI 穩定性,再結合機器學習演算法,以改善即時室內定位準確度。 本文提出的即時定位方法採用iBeacon及Android 智慧型手機作為實驗設備進行測試,並比較K最近鄰居(KNN)、支援向量機(Support Vector Machine, SVM)及隨機森林(Random Forest)演算法之差異,透過實驗結果顯示,在室內定位的訊號收集密度1公尺左右,定位準確度為最佳。zh_TW
dc.description.abstractIn recent years, outdoor positioning technology has been quite mature, but subject to the indoor environment for the obstacles such as GPS satellite signals, many research and discussion on indoor positioning technology has been launched, and the technology with lower cost and convenient construction method is more convenient, such as Bluetooth, Wi-Fi, etc. This paper is based on the 2013 iBeacon proposed by Apple Inc. The Bluetooth signal RSSI value drifting seriously, which will result in poor positioning. At present, most of the literature on indoor positioning mainly proposes how to apply technology to achieve and improve positioning. There are few discussions about the relationship between density of signals collection and accuracy of positioning[1]. According to the experimental results of this study, under the same conditions, the higher the positioning signal collection density, the lower the positioning accuracy. This paper explores the inaccuracy of the Beacon positioning device built in the Art Center of National Chung Hsing University, and attempts to propose a set of algorithms that can be used to improve the accuracy of existing indoor positioning. In this study, the Kalman Filter (KF) is used to improve the stability of the Bluetooth signal RSSI, combined with machine learning algorithms to improve the accuracy of the indoor location. The instant positioning method proposed in this paper uses iBeacon and Android smart phone as experimental devices to test and compare the differences between K nearest neighbor (KNN), support vector machine (SVM) and random forest (Random Forest) algorithms. The experimental results show that when the indoor positioning signal is collected at a density of about 1 meter, the positioning accuracy is optimal.en_US
dc.description.tableofcontents摘要 i Abstract ii 目錄 iii 表目錄 v 圖目錄 vi 第一章 緒論 1 1.1 研究動機 1 1.2 研究目的 2 1.3 論文架構 3 第二章 文獻探討 4 2.1 定位技術發展沿革 4 2.2 定位感測技術 7 2.2.1 Bluetooth 7 2.2.2 Wi-Fi 8 2.2.3 ZigBee 8 2.2.4 RFID(Radio Frequency Identification) 8 2.2.5 紅外線 8 2.2.6 UWB 9 2.2.7定位感測技術比較 9 2.3 訊號衰減特性探討 10 2.3.1 影響藍芽裝置訊號品質因素 10 2.3.2 藍芽訊號傳遞特性 10 2.4 訊號濾波器 13 2.4.1 均值濾波器(Mean Filter) 13 2.4.2 中值濾波器(Median Filter) 14 2.4.3 眾數濾波器(Mode Filter) 14 2.4.4 卡爾曼濾波器(Kalman Filter) 14 2.5 定位演算法 16 2.5.1 三角定位法(Triangulation) 16 2.5.2 訊號紋定位法(Fingerprinting) 19 2.5.3 定位演算法比較 19 2.6 機器學習 20 2.6.1 最近鄰居法(K Nearest Neighbor, KNN) 20 2.6.2 支援向量機(Support Vector Machine, SVM) 20 2.6.3 隨機森林(Random Forest) 21 2.6.4 交叉驗證 21 2.6.5 機器學習演算法比較 21 第三章 研究方法 23 3.1 實驗流程圖 23 3.1.1 規劃階段 24 3.1.2 收集階段 28 3.1.3 學習訓練階段 29 3.1.4 預測階段 29 3.2 定位流程圖 29 第四章 實驗結果 31 4.1實驗設備 31 4.1.1 智慧型行動裝置 31 4.1.2 藍芽訊號發射器 33 4.2 軟體工具 35 4.2.1 智慧型行動裝置定位程式開發平台 35 4.2.2 單機數據分析程式開發平台 35 4.3 實驗施測場地 36 4.3.1 實驗場域一 36 4.3.2 實驗場域二 38 4.4實作 39 4.4.1 排除掉環境中來自與本研究無關的藍芽設備 RSSI 39 4.4.2 收集RSSI數據APP 40 4.4.3 手機定位APP 41 4.5 實驗結果 43 4.5.1 RSSI 收集與分析 43 4.5.2機器學習演算法準確度比較 47 第五章 結論 51 參考文獻 52zh_TW
dc.language.isozh_TWzh_TW
dc.rights同意授權瀏覽/列印電子全文服務,2022-01-25起公開。zh_TW
dc.subject室內定位zh_TW
dc.subjectiBeaconzh_TW
dc.subjectRSSIzh_TW
dc.subject卡爾曼濾波器zh_TW
dc.subjectK最近鄰居演算法zh_TW
dc.subject支援向量機演算法zh_TW
dc.subject隨機森林演算法zh_TW
dc.subjectIndoor positioningen_US
dc.subjectiBeaconen_US
dc.subjectRSSIen_US
dc.subjectKalman filteren_US
dc.subjectK nearest neighbor algorithmen_US
dc.subjectsupport vector machine algorithmen_US
dc.subjectrandom forest algorithmen_US
dc.title基於卡爾曼濾波器與機器學習之低功耗藍芽裝置室內定位準確度強化技術zh_TW
dc.titleAccuracy Enhancement Technology of BLE Device Indoor Positioning System Based on Kalman Filter and Machine Learningen_US
dc.typethesis and dissertationen_US
dc.date.paperformatopenaccess2019-01-25zh_TW
dc.date.openaccess2022-01-25-
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item.openairetypethesis and dissertation-
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