臺灣博碩士論文加值系統

在物聯網逐漸普及的時代中各種室內定位方法也隨之出現，現今常見的定位方法不外乎就是使用訊號強度或是訊號到達時間差等方法，但是各種方法都會產生訊號干擾導致定位準度降低等問題，如無線傳輸中遇到金屬或人體等干擾源即會使接收訊號強度十分不穩定，而本文提出一種使用藍牙接收訊號強度配合九軸姿態感測器的定位方法，本文中利用藍牙接收訊號強度之定位方法結合九軸姿態感測器，並使用經過所推導出來之歐拉角結合運算，再以電腦端之C#程式開發一套包括使用者介面的室內定位監視管理系統，其中功能包含了MQTT訊息訂閱與藍牙封包解析等，並在執行時可以即時觀看目前感測器所在位置等。最後將兩感測數值以C#程式結合使用後去做到完整的室內定位功能，實驗時本文以僅使用藍牙接收訊號強度之定位方法與配合九軸感測器之藍牙接收訊號強度定位方法分別做定位實驗，而結果也驗證了配合九軸姿態感測器的定位方法可以有降低錯誤率與定位點彈跳的發生。

In the era of Internet of Things, multiple indoor positioning methods have also attracted attention. Nowadays, the common positioning methods use signal strength or the time difference in the arrival of the signal, but these methods cause signal interference, which results in a reduced positioning precision. The received signal strength (RSS) becomes very unstable in wireless transmission due to interference which is caused by sources such as a metal or human body. This study aims to propose a positioning method that uses Bluetooth to receive signal strength combined with a nine-axis motion sensor. The Bluetooth positioning part locates the position of the object to be tested by detecting the common RSS. In the nine-axis motion sensor part, the value obtained by the sensor is calculated through the derived Euler angle formula. C# is used to develop a set of indoor positioning software that includes a user interface. The functions include MQTT message subscription and Bluetooth packet parsing. The location of the current sensor in execution can be detected instantly. Finally, the two sensed values are combined using the C# program to achieve a complete indoor positioning function. In this positioning experiment, the positioning method by detecting Bluetooth RSS and the Bluetooth receiving signal strength positioning method combined with the nine-axis sensor are separately conducted. The results also prove that the positioning method combined with the 9-axis motion sensor can reduce the error rate and occurrence of positioning point bouncing.

摘要...........................i
Abstract...........................ii
誌謝...........................iii
目錄...........................iv
表目錄...........................vi
圖目錄...........................vii
第一章 緒論..........................1
1.1 研究背景與動機..........................1
1.2 研究目的與方法..........................1
第二章 相關技術探討..........................2
2.1 藍牙通訊..........................2
2.2 Wi-Fi通訊..........................5
2.3 MQTT通訊..........................6
2.4 九軸姿態感測儀..........................9
2.5 感測器資料傳輸介面..........................13
2.6 室內定位技術..........................16
2.6.1 訊號強度定位法(Received signal strength indicator , RSSI)...16
2.6.2 到達時間差定位法(Time of Arrival, TOA)..........................17
2.6.3 指紋定位法(Fingerprint)..........................18
第三章 感測資料前處理..........................19
3.1 系統架構..........................19
3.2 歐拉角推導..........................20
3.3 九軸感測重力抽離..........................26
3.4 磁力計校正..........................27
第四章 系統設計與實作..........................29
4.1 低功耗藍牙元件設計..........................29
4.1.1 藍牙iBeacon設定..........................30
4.1.2 藍牙Gateway設定..........................32
4.2 Wi-Fi元件設計..........................33
4.2.1 Wi-Fi連線..........................33
4.2.2 MQTT連線發送..........................33
4.3 九軸姿態感測融合使用..........................35
4.3.1 陀螺儀歐拉角計算..........................35
4.3.2 互補濾波器..........................35
4.3.3 磁力計融合..........................36
4.4 定位監視管理程式設計..........................38
第五章 系統實測..........................40
5.1 實驗設備..........................40
5.2 運動偵測..........................44
5.2.1 藍牙RSSI..........................44
5.2.2 九軸姿態感測..........................47
5.3 室內定位實驗..........................49
5.3.1 環境建置..........................49
5.3.2 系統實測..........................52
5.3.3 數據結果與分析..........................55
第六章 結論與未來展望..........................56
參考文獻..........................57
Extended Abstract..........................59

[1]W. Mir et al., “GPS Technology,” IEEE Students Conference, ISCON '02. Proceedings., Lahore.,2003,pp.27-39.
[2]廖子翔，2014，“智慧型行動裝置之 Wi-Fi 與 Bluetooth 混合定位技術室內導覽系統實現與優化”，國立虎尾科技大學電機工程系研究所碩士論文.
[3] Yixin Wang et al., “RSSI-Based Bluetooth Indoor Localization,” IEEE International Conference on Mobile Ad-hoc and Sensor Networks., Shenzhen.,2015,pp.165-171.
[4]Despina Michael-Grigoriou et al., “Intuitive Interaction for Exploring Human Anatomy
in a VR Setup,” IEEE International Conference on Virtual System & Multimedia (VSMM)., Dublin.,2017.
[5]Shaopeng Liu et al., “Multisensor Data Fusion for Physical Activity Assessment,” IEEE Transactions on Biomedical Engineering.,2011,pp687-696.
[6]網路資料:“BLE簡介”, http://shyuanliang.blogspot.tw/2014/10/
[7]網路資料:“藍芽架構圖”http://shyuanliang.blogspot.com/2014/10/
[8]鄧友清，“高速無線區域網路時代即將到來”，新通訊元件雜誌，2001。
[9]網路資料: “ Linux Wireless-無線操作模式” http://shaocheng.li/post/blog/2012-10-27-wireless-oprating-mode
[10]網路資料: “ MQTT簡介” https://makehub.tw/communication/mqtt/mqtt-introduction.
[11]網路資料: “ 淺談MQTT通訊協定”, http://iotforum.advantech.com/discussion/11/%E6%B7%BA%E8%AB%96mqtt%E9%80%9A%E8%A8%8A%E5%8D%94%E5%AE%9A
[12]網路資料:“MPU9250 datasheet”https://www.invensense.com/download-pdf/mpu-9250-datasheet/
[13]網路資料: “加速度計原理” http://www.ni.com/white-paper/3807/zht/
[14]Sina Askari et al., “Multisensor Data Fusion for Physical Activity Assessment,” IEEE International Symposium on Inertial Sensors and Systems (INERTIAL)., Italy.,2018.
[15]網路資料: “陀螺儀” https://zh.wikipedia.org/wiki/%E9%99%80%E8%9E%BA%E5%84%80
[16]網路資料:“地磁計工作理”,http://blog.sina.com.cn/s/blog_402c071e0102v8ig.html
[17]Liu Chunyang et al., “Gesture detection and data fusion based on MPU9250 sensor,” IEEE International Conference on Electronic Measurement & Instruments (ICEMI)., qingdao., 2015, pp. 1612-1615.
[18]網路資料: “I^2 C”, https://zh.wikipedia.org/wiki/I%C2%B2C.
[19]網路資料: “UART”, https://zh.wikipedia.org/wiki/UART.
[20] 網路資料: “UART區塊結構圖”, http://ir.lib.cyut.edu.tw:8080/bitstream/310901800/33110/11
[21]Fu Zhou et al., “RSSI indoor localization through a Bayesian strategy,” Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)., China.,2017.
[22]Moises Granados-Cruz et al., “Triangulation-based indoor robot localization using extended FIR/Kalman filtering,” International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)., Mexico.,2014.
[23]Rafal Szumny et al.,“Influence of antennas characteristics on accuracy of TOA indoor positioniong systems” International Conference on Microwaves, Radar and Wireless Communications., Poland.,2008.
[24]R.Kaune, “Accuracy Studies for TDOA and TOA Localization,” 2012 15th International Conference on Information Fusion, pp. 408-415, 2012.
[25]王妍之，2013，“以到達時間(TOA)及到達時間差(TDOA) 為基礎之聲源定位法”，國立清華大學動力機械工程學系研究所碩士論文。
[26]M.Stella , M.Russo , and D.Begusic, “Location Determination in Indoor Environment based on RSS Fingerprinting and Artificial Neural Network,” Telecommunications, 2007. ConTel 2007. 9th International Conference on, pp. 301-306, June 2007.
[27]M.El Hassan , B.El Hassan , and L.Nachabe, “Implementation of wireless network using location fingerprinting technique for indoor positioning,” Electrotechnical Conference (MELECON), 2012 16th IEEE Mediterranean, pp. 216-219, March 2012.
[28] 網路資料: “歐拉角推導”, http://silverwind1982.pixnet.net/blog/post/258069682
[29]Y M Zhao, H L Chang, G M Yuan et al., “Micro AHRS algorithm based on three-component geomagnetic filtering technology” Mechanical & Electrical Engineering Journal., 2014., vol. 31, no. 6, pp. 745-748.