國立虎尾科技大學 |

Language: English

Back

使用藍牙測向與機器學習技術強化室內定位系統之研製 = = Develop...

張弘恩

使用藍牙測向與機器學習技術強化室內定位系統之研製 = = Development of an Indoor Positioning System Enhanced by Bluetooth Direction Finding and Machine Learning Techniques /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	使用藍牙測向與機器學習技術強化室內定位系統之研製 =/ 張弘恩.
Reminder of title:	Development of an Indoor Positioning System Enhanced by Bluetooth Direction Finding and Machine Learning Techniques /
remainder title:	Development of an Indoor Positioning System Enhanced by Bluetooth Direction Finding and Machine Learning Techniques.
Author:	張弘恩
Published:	雲林縣 :國立虎尾科技大學 , : 民113.07.,
Description:	[10], 92面 :圖, 表 ; : 30公分.;
Notes:	指導教授: 蘇暉凱.
Subject:	Bluetooth Direction Finding. -
Online resource:	電子資源

使用藍牙測向與機器學習技術強化室內定位系統之研製 = = Development of an Indoor Positioning System Enhanced by Bluetooth Direction Finding and Machine Learning Techniques /
張弘恩

使用藍牙測向與機器學習技術強化室內定位系統之研製 =Development of an Indoor Positioning System Enhanced by Bluetooth Direction Finding and Machine Learning Techniques /Development of an Indoor Positioning System Enhanced by Bluetooth Direction Finding and Machine Learning Techniques.張弘恩. - 初版. - 雲林縣 :國立虎尾科技大學 ,民113.07. - [10], 92面 :圖, 表 ;30公分.

指導教授: 蘇暉凱.

碩士論文--國立虎尾科技大學電機工程系碩士班.

含參考書目.

本論文提出一個透過少量藍牙測向天線結合機器學習強化室內定位性能的架構與方法，包含整合原有的室內定位與燈具控制以及縮短定位所需時間，使本論文開發的藍牙AoA-Dongle能與原有的Dongle兼容於同一Mesh網路並具有藍牙測向功能且提供更快速的即時定位。Mesh網路蒐集到的數據會經由藍牙Gateway以MQTT傳輸至主機端進行定位計算。為了完成以上功能，我們以nRF5340與u-blox AoA天線板為基礎，將兩者結合開發成用於接收藍牙Dongle並實裝於實驗場域中。場域中我們以網格型式布置了12個定位點，並於各點蒐集數據組成資料集，這些資料集以使用的藍牙測向AoA天線數量及是否進行資料預處理做分類，並用於訓練以下四種機器學習模型：K-近鄰演算法（K-Nearest Neighbors, KNN）、支持向量機（Support Vector Machine, SVM）、隨機森林（Random Forest, RF）和多層感知器(Multilayer Perceptron, MLP)，並通過網格搜尋法找到四種模型的最佳參數組合，最後再使用混淆矩陣、F1-Score等指標驗證各模型的性能。從實驗結果可得知，採用一個及兩個AoA天線特徵時的模型的準確率較原系統未採用時來的高，比起使用原始資料集(即原系統)訓練模型時分別提升11.11%~30.51%不等；在預處理資料集中則分別提升1.17%~6.32%不等。將四種模型均放入定位系統中實時預測，其中以支持向量機表現最佳，取得高達96.58%的準確度。.

(平裝)Subjects--Topical Terms:

1451826
Bluetooth Direction Finding.

使用藍牙測向與機器學習技術強化室內定位系統之研製 = = Development of an Indoor Positioning System Enhanced by Bluetooth Direction Finding and Machine Learning Techniques /
LDR:04350cam a2200241 i 4500 001 1129940
008 241015s2024 ch ak erm 000 0 chi d
035 $a (THES)112NYPI0441024
040 $a NFU $b chi $c NFU $e CCR
041 0 # $a chi $b chi $b eng
084 $a 008.165M $b 1116:2 113 $2 ncsclt
100 1 $a 張弘恩 $3 1448980
245 1 0 $a 使用藍牙測向與機器學習技術強化室內定位系統之研製 = $b Development of an Indoor Positioning System Enhanced by Bluetooth Direction Finding and Machine Learning Techniques / $c 張弘恩.
246 1 1 $a Development of an Indoor Positioning System Enhanced by Bluetooth Direction Finding and Machine Learning Techniques.
250 $a 初版.
260 # $a 雲林縣 : $b 國立虎尾科技大學 , $c 民113.07.
300 $a [10], 92面 : $b 圖, 表 ; $c 30公分.
500 $a 指導教授: 蘇暉凱.
500 $a 學年度: 112.
502 $a 碩士論文--國立虎尾科技大學電機工程系碩士班.
504 $a 含參考書目.
520 3 $a 本論文提出一個透過少量藍牙測向天線結合機器學習強化室內定位性能的架構與方法，包含整合原有的室內定位與燈具控制以及縮短定位所需時間，使本論文開發的藍牙AoA-Dongle能與原有的Dongle兼容於同一Mesh網路並具有藍牙測向功能且提供更快速的即時定位。Mesh網路蒐集到的數據會經由藍牙Gateway以MQTT傳輸至主機端進行定位計算。為了完成以上功能，我們以nRF5340與u-blox AoA天線板為基礎，將兩者結合開發成用於接收藍牙Dongle並實裝於實驗場域中。場域中我們以網格型式布置了12個定位點，並於各點蒐集數據組成資料集，這些資料集以使用的藍牙測向AoA天線數量及是否進行資料預處理做分類，並用於訓練以下四種機器學習模型：K-近鄰演算法（K-Nearest Neighbors, KNN）、支持向量機（Support Vector Machine, SVM）、隨機森林（Random Forest, RF）和多層感知器(Multilayer Perceptron, MLP)，並通過網格搜尋法找到四種模型的最佳參數組合，最後再使用混淆矩陣、F1-Score等指標驗證各模型的性能。從實驗結果可得知，採用一個及兩個AoA天線特徵時的模型的準確率較原系統未採用時來的高，比起使用原始資料集(即原系統)訓練模型時分別提升11.11%~30.51%不等；在預處理資料集中則分別提升1.17%~6.32%不等。將四種模型均放入定位系統中實時預測，其中以支持向量機表現最佳，取得高達96.58%的準確度。.
520 3 $a This thesis proposes a framework and method to enhance indoor positioning performance by utilizing a small number of Bluetooth direction-finding antennas combined with machine learning. The framework integrates existing indoor positioning and lighting control, allowing the developed Bluetooth AoA-Dongle to be compatible with existing dongles within the same mesh network while providing Bluetooth direction-finding functionality. To achieve this, we used the nRF5340 and u-blox AoA antenna boards as the basis, combining them to develop a system that can receive Bluetooth dongles and implement it in an experimental field. We arranged 12 positioning points in a grid pattern and collected data at each point to form a dataset. These datasets were categorized based on the number of Bluetooth AoA antennas used and whether data preprocessing was performed, and were used to train the following four machine learning models: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), and Multilayer Perceptron (MLP). The optimal parameter combinations for the four models were found using the grid search method. Finally, the performance of each model was validated using confusion matrices and F1-Score metrics. Experimental results indicate that the models' accuracy, when using one or two AoA antenna features, is higher than the original system without the feature, showing an improvement ranging from 11.11% to 30.51% compared to using the original dataset (i.e., the original system). In the preprocessed datasets, the improvement ranges from 1.17% to 6.32%. All four models were then incorporated into the positioning system for real-time prediction, with the Support Vector Machine performing the best, achieving an accuracy of up to 96.58%..
563 $a (平裝)
650 # 4 $a Bluetooth Direction Finding. $3 1451826
650 # 4 $a MQTT Protocol. $3 1339806
650 # 4 $a BLE Mesh Network. $3 1339805
650 # 4 $a Machine Learning. $3 1137723
650 # 4 $a Indoor Positioning. $3 1151214
650 # 4 $a MQTT通訊協定. $3 1339802
650 # 4 $a 藍牙網狀網路. $3 1339801
650 # 4 $a 藍牙測向. $3 1451825
650 # 4 $a 機器學習. $3 995923
650 # 4 $a 室內定位. $3 997488
856 7 # $u https://handle.ncl.edu.tw/11296/4398bg $z 電子資源 $2 http