
系統識別號 
U00260408201615031900 
論文名稱(中文) 
使用低耗電藍牙裝置與樸素貝式分類法之室內定位技術 
論文名稱(英文) 
Indoor Positioning Using Bluetooth Low Energy Devices and Naive Bayes Classification 
校院名稱 
成功大學 
系所名稱(中) 
工程科學系 
系所名稱(英) 
Department of Engineering Science 
學年度 
104 
學期 
2 
出版年 
105 
研究生(中文) 
王博威 
研究生(英文) 
PoWei Wang 
學號 
n96034154 
學位類別 
碩士 
語文別 
英文 
論文頁數 
68頁 
口試委員 
指導教授侯廷偉 口試委員王明習 口試委員鄧維光 口試委員席家年

中文關鍵字 
室內定位
低耗電藍牙
樸素貝式分類器

英文關鍵字 
Indoor Positioning
BLE
naive Bayes classifier

學科別分類 

中文摘要 
室內定位系統的評比，除了考量定位精準度外，建造成本亦在衡量範圍內，以現行常見的定位技術而言，特徵定位法精準度高、佈建時間成本高，三角定位法精準低，但佈建時間成本低，本研究目標為結合特徵定位法的優點與三角定位法的優點，提出一套能降低時間成本但提高定位精準度之室內定位演算法，本研究採用iBeacon作為訊號源，iBeacon由低耗電藍牙(BLE, Bluetooth Low Energy)發展而成，由於BLE頻寬窄，導致訊號衰減快速，造成傳統距離轉換模型沒辦法使用在BLE訊號源上，離訊號源愈遠所造成的轉換誤差愈大，本研究以混和濾波器減少訊號震盪，並利用樸素貝式分類器對距離藉由訊號強度進行分類，最後在使用最小平方估計法進行多點定位，得到定位結果，實驗結果顯示，比較傳統距離轉換模型，更能提升角落的定位精準度，使用樸素貝式分類器之準確率於1.5公尺內次數可達80%，而使用LAM(Linear Approximation Model)之準確率於1.5公尺內次數僅有48%，利用本研究所提出之室內定位演算法進行小區塊分類結果其準確率與特徵定位演算法相近。

英文摘要 
The efficiency of an indoor positioning system can be evaluated by its positioning accuracy and building cost. There are currently two representative indoor positioning techniques. Fingerprinting has high positioning accuracy and high building time cost, and triangulation has less positioning accuracy and less building time cost. The purpose of our research is to combine the advantages of both the fingerprinting and triangulation techniques in order to propose a new indoor positioning algorithm that can reduce building time and can enhance positioning accuracy. Our research takes iBeacon, which is extended from BLE (Bluetooth Low Energy) as transmitters. Due to BLE’s narrow channel space, BLE signal strength descends rapidly. This makes Linear Approximation Model not feasible in distance translation. The farther from transmitter, the more distance translating error. In this research, the hybrid filter is used to converge oscillating signal values, and the naive Bayes classifier is used to classify distance according to signal strength. Finally, more than three transmitters are used to calculate the location of objects using least square estimation. The experiment results show the proportion of positioning error using the naive Bayes classifier at 1.5 meters is about 80%, and the proportion of translating distance by LAM is only 48%. Compared to LAM, the proposed indoor positioning algorithm can increase positioning accuracy at corners, and compared to the fingerprinting method, the proposed indoor algorithm has nearly the same positioning accuracy.

論文目次 
摘要.................... ................I
Abstract................................II
致謝....................................III
Table of Contents.......................IV
List of Tables..........................VI
List of Figures.........................VII
Chapter 1 Introduction..................1
1.1 Background..........................1
1.2 Motivation and Purpose..............3
1.3 Organization of Thesis..............4
Chapter 2 Related Work..................5
2.1 Indoor Positioning System...........5
2.2 Signal Filter.......................6
2.2.1 Moving Average Filter.............7
2.2.2 Weight Filter.....................8
2.2.3 Window Selection Filter...........9
2.2.4 Kalman Filter.....................9
2.3 Triangulation.......................10
2.3.1 Angle of Arrival..................10
2.3.2 Distance Method...................12
2.3.2.1 Time of Arrival (TOA)...........12
2.3.2.2 Time Difference of Arrival......14
2.3.2.3 Received Signal Strength........15
2.3.2.4 Free Space Model................17
2.3.2.5 Two Ray Ground Model............18
2.3.2.6 Shadowing Model.................20
2.3.2.7 Linear Approximation Model......20
2.4 Linear Regression...................21
2.5 Least Square Estimation.............23
2.6 Fingerprinting......................24
2.7 Naive Bayes.........................27
2.8 MultiFloor Positioning.............29
Chapter 3 Proposed Algorithm............30
3.1 Proposed Approach...................30
3.2 Wireless Source.....................32
3.3 Linear Approximation Model VS. Naive Bayes Classifier 38
3.4 Positioning Algorithm...............40
Chapter 4 Experiments and Results.......44
4.1 Experimental Devices................44
4.2 Indoor Positioning Experiment.......45
4.2.1 Area one..........................45
4.2.2 Area two..........................49
4.2.3 Block three.......................53
4.3 Tracking Experiment.................57
4.4 Discussion..........................60
Chapter 5 Conclusion and Future Work....61
5.1 Conclusion..........................61
5.2 Future Work.........................62
References..............................63
Appendix A Multifloor Positioning Method.......67
Table 11 Comparison of Indoor Positioning Technologies [3] [5].....2
Table 31 Standard Deviation from one to eight meters.....37
Table 41 Details of Transmitters in area one.....46
Table 42 Comparison of the proposed algorithm to fingerprinting in area one.....49
Table 43 Details of transmitters in area two.....50
Table 44 Comparison of the proposed algorithm to fingerprinting in area two.....53
Table 45 Details of transmitters in area three.....54
Table 46 A comparison of the proposed algorithm to fingerprinting in area three.....57
Figure 2.1 Indoor positioning technologies [3].....6
Figure 2.2 Multipath propagation.....7
Figure 2.3 Kalman filter [10].....10
Figure 2.4 AOA algorithm.....11
Figure 2.5 TOA algorithm.....14
Figure 2.6 TDOA algorithm.....15
Figure 2.7 Flow of proposed algorithm.....16
Figure 2.8 TRGM.....19
Figure 2.9 Same trend line with different data [18].....22
Figure 2.10 The positioning of four transmitters.....24
Figure 2.11 Signal conflict in center.....25
Figure 2.12 Flow of fingerprinting.....26
Figure 3.1 Flow of proposed algorithm.....31
Figure 3.2 BLE channels and the three most commonly used WiFi channels [9].....33
Figure 3.3 iBeacon packet format [22].....33
Figure 3.4 1D Kalman filter.....34
Figure 3.5 RSSI measured at one meter with Kalman filter.....35
Figure 3.7 RSSI measured at one meter with the hybrid filter.....37
Figure 3.8 Signal strength from one to eight meters.....38
Figure 3.9 LAM : RSS=19.3258×logd69.4657.....39
Figure 3.10 Naïve Bayes classifier classified result.....40
Figure 3.11 The concept of the correction method.....43
Figure 4.1 Experimental devices.....44
Figure 4.2 Floor plan of the laboratory.....46
Figure 4.3 Positioning error and standard deviation in area one.....47
Figure 4.4 Proportion of position error within 1 and 1.5 meters in area one.....48
Figure 4.5 Floor plan of the meeting room.....50
Figure 4.6 Positioning error and standard deviation in area two.....51
Figure 4.7 The proportion of position error within 1 and 1.5 meters in area two.....52
Figure 4.8 Floor plan of the public space.....54
Figure 4.9 Positioning error and standard deviation in area three.....55
Figure 4.10 Proportion of position error within 1 and 1.5 meters in area three.....56
Figure 4.11 The results of the first tracking experiment.....58
Figure 4.12 The results of the second tracking experiment.....60

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