Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/98245
標題: 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
Accuracy Enhancement Technology of BLE Device Indoor Positioning System Based on Kalman Filter and Machine Learning
作者: 廖健智 
Jian-Zhi Liao 
關鍵字: 室內定位;iBeacon;RSSI;卡爾曼濾波器;K最近鄰居演算法;支援向量機演算法;隨機森林演算法;Indoor positioning;iBeacon;RSSI;Kalman filter;K nearest neighbor algorithm;support vector machine algorithm;random forest algorithm
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摘要: 
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。

In 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.
URI: http://hdl.handle.net/11455/98245
Rights: 同意授權瀏覽/列印電子全文服務,2022-01-25起公開。
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