本論文中將未來的高精準度定位分為三個方向，室內的精準定位、室內的樓層判定、車輛的危險判定，來進行模擬與分析。
在未來的環境中，在一定的區域範圍內，會有分布密集的通訊裝置，這些裝置都需感測鄰近的資訊，透過無線通訊將之資訊上傳並回報雲端，而在這些大數量的裝置設備下，作為大數量資訊的收集，這些資訊的回報必會有射頻端的傳接收機，所以當訊號發送出去時，會將其本身裝置的位置傳送出去，也可以用來當作定位資訊，當收到夠多的定位資訊時，在傳統的三角定位中就可以來提供更高精準度定位的實現，在本論文中提出使用藍芽終端設備，達到九個以上，就能小於 1 公尺以下的誤差範圍，並使用權重的分配使得八個以上，就能小於 1 公尺以下的誤差範圍。
在室內的樓層偵測中，每一層樓布置多個微小型基地台，並利用 RSSI 特性，來進行樓層的偵測，可以達到 99%的樓層判別精準度，而且提出在某樓層若有斷電的情況下，本論文利用還存活的微小型基地台，提出一差異化平均的演算法，來對於斷電樓層的使用者定位出其所在樓層，在頂樓與底樓的斷電情況下，可以達到82.79%的精準度，而在中間樓層斷電情況下，可以達到 91.44% 的精準度。
在未來對於低延遲的訊息將會非常的要求，因為低延遲可以有更多的時間處理訊息，或是可以更即時通報重要訊息，所以在低延遲下，如何非常快速的定位出目前的相對位置，是非常重要的，在 V2V 中，車輛是可以即時的互相傳送訊息，而車輛的行駛與前後方車輛的位置，都是隨時在移動，所以位置的訊息無法很精確，本論文利用都普勒特性，對於移動中車輛所發出訊號的頻率飄移，來分析出目標與本身之間的動態關係，透過在車輛行駛中，頻率飄移的變化偵測出同車道的車輛，可以達到 94%的機率，並透過頻率飄移的接收值，來偵測出危險車輛的判定。綜合上述的研究項目，在 5G 高精準度定位下的實現，可以為人們的生命安全提供快速、緊急、準確的定位資訊。綜合上述的研究項目，在5G高精準度定位下的實現，可以為人們的生命安全提供快速、緊急、準確的定位資訊。

In this dissertation it considers the development of future high accuracy positioning technique from combining the methodologies developed for indoor accuracy positioning, indoor floor detection and dangerous vehicle detection.  
In future communication networks, it has dense of communication devices launched in a communication area; these devices sense and monitor neighbor devices status and convey these information to the cloud after the cloud processes these large amount of information; these information are conveyed by RF transceivers and simultaneously the locations of these transceivers are transmitted together with these messages.
Therefore when devices messages are transmitted their location information is simultaneously transmitted that this location information can be processed to estimate devices locations. In the traditional triangular positioning when more positioning information are available, it can estimate and generate more accurate location information. In triangular positioning algorithm when it has more than nine devices locations available it can result in less than one foot positioning error and furthermore when weighting factors are introduced among the positioning data it can reach one foot positioning error by using only eight devices location information. In the indoor floor detection, small cells are launched in each floor and it uses the received cells RSSI information and uses two developed algorithms, i.e., Maximum Cell RSSI and Average Cells RSSI, to perform floor detection; it can reach 99% accuracy in floor detection when all small cells in the floor are normally operating. If power fails in the top or the bottom floor a Differential Average Cells RSSI algorithm is developed it can attain 82.79% accuracy in the floor detection while it can reach 91.4% accuracy in the floor detection when a floor other than the top and the bottom floor has power failure. 
It has stringent low latency transmission requirement in future high speed mobile communication and due to short time spent in the message transmission, it has more time available to process the message to have the message transmitted almost in real time, consequently it is very important to develop a fast positioning algorithm to accurately locate the position of an object considered in the low latency transmission environmrnt. In V2V communication when a vehicle is traveling its location and the locations of the vehicles traveling in its front and back are varying continuously with time; in this dissertation it uses the Doppler shift principle to calculate the frequency shifts of moving vehicles and uses these information to analyze the dynamic relationships among vehicles and then uses the timing variation of these Doppler shifts to detect and find vehicles that are moving in the same lane it can reach 94% correct probability in determining the vehicles traveling in the same lane. And also from manipulating time varying Doppler shifts it defines a dangerous index for each vehicle, up to five dangerous indexes defined, to define the dangerous level a vehicle will incur in possible vehicles collision when vehicles are traveling in the same direction; and also a dangerous zone is defined that when a vehicle moves into this dangerous zone vehicle collisions may occur.
From combining the positioning methodologies developed for indoor accuracy positioning, indoor floor detection and dangerous vehicle detection  we can develop high accuracy positioning algorithms for 5G and B5G mobile networks to provide fast, accurate and emergent positioning information for protecting human life and security.

CHINESE ABSTRACT	I
ENGLISH ABSTRACT	III
TABLE OF CONTENTS	VII
LIST OF FIGURES	X
CHAPTER 1	INTRODUCTION	1
1.1	Study Motivation	1
1.1.1 Review 3GPP positioning development	3
1.1.2 Review of Related Work	6
1.2	Organization	8
CHAPTER 2	ACCURATE BLUETOOTH POSITIONING USING WEIGHTING RSSI AND LARGE NUMBER OF DEVICES MEASUREMENTS	12
2.1	Introduction	12
2.2	Field Trial Scenario and Data Measurement Functional Flock Diagram	15
2.3	RSSI Measurement	17
2.4	Localization Scheme	22
2.4.1 Two- point Positioning Method	24
2.4.2 Three-point Positioning Method	25
2.5	Accuracy Enhancement Positioning Method	26
2.5.1 Weighting Among Measured Data	26
2.5.2 Generation of Large Number of Test Points	27
2.6	Simulation Results	28
2.7	Conclusion	29
CHAPTER 3	FLOOR DETECTION METHODOLOGY	31
3.1	Introduction	31
3.2	Channel Model	33
3.2.1 Indoor Channel Model	33
3.2.2 Link Budget	34
3.2.3 Indoor Scenario	36
3.3	Floor Detection Method	37
3.3.1 Maximum Cell RSSI Algorithm	37
3.3.2 Average Cells RSSI Algorithm	38
3.3.3 Differential Average Cells RSSI Algorithm	39
3.4	Simulation Result	41
3.4.1 Simulation Result of Floor Detection	41
3.4.2 Simulation Results in Small Cells Positioning	45
3.4.3 Power Failure in Floor Detection	45
3.4.3.1	Power Fail in All Floor 2 Small Cells	45
3.4.3.2	Power Fail in All Floor 1 Small Cells	49
3.5	Conclusion	53
CHAPTER 4	STUDY ON HIGH SPEED VEHICLES PRE-WARNING SYSTEM	57
4.1	Introduction	57
4.2	Doppler Shift	61
4.3	Same Lane Detection Methodology	62
4.4	Dangerous Vehicle Detection Methodology	68
4.5	V2V Scenario in High Speed Environment	74
4.6	Conclusion	81
CHAPTER 5	CONCLUSION AND FUTURE WORK	82
5.1	Future Work of High-Accuracy Positioning	82
5.1.1 High-Density Population Positioning	82
5.1.2 Outdoor/Indoor 3D positioning	82
5.1.3 Floor Detection	84
5.1.4 Vehicles Collision Avoidance	85
5.1.5 Under Water Communications	87
5.2	Conclusion	87
REFERENCES	90
PUBLICATION	98

LIST OF FIGURES
Figure 1 Current Positioning Systems According to Their Accuracy and Coverage Area	5
Figure 2  An Example of Network Supporting High Accuracy Positioning [22]	7
Figure 3 Measurement Environment - Third Floor, Engineering Building, Tamsui Campus, Tamkang University	16
Figure 4 Device Used in The Transmission Test: Samsung Galaxy S3 Cellphone	16
Figure 5 System Functional Block Diagram in Target Location Determination	17
Figure 6 Signal Strength Measurement Environment	19
Figure 7 LOS Field Measurements	19
Figure 8 BT Measured and Simulated Fading Condition	21
Figure 9 BT Path Loss Model	21
Figure 10 Locating a Cellphone from Multiple Cellphones	23
Figure 11 Field Trial Environment	23
Figure 12 Two-point Positioning Method	24
Figure 13 Distance Determination Errors with and without Implementing the Weighting Factors and RSSI Selection Processes	29
Figure 14  The Dual- Stripe Scenario [48]	34
Figure 15 Indoor Channel Model with Different n	34
Figure 16 Pr with Different n	36
Figure 17 Indoor Scenario	37
Figure 18  Effect of Value n on Floor Detection Errors	41
Figure 19 CDF of Floor Detection Errors	42
Figure 20 CDF of Floor Detection Errors Using the Maximum Cell RSSI Algorithm	43
Figure 21 Number of Floor Detection Errors Using the Maximum Cell RSSI Algorithm as Simulation Time Evolves	43
Figure 22 CDF of Floor Detection Errors Using the Average Cells RSSI Algorithm	44
Figure 23 Floor Detection Errors Using the Average Cells RSSI Algorithm as Simulation Time Evolves	44
Figure 24 CDF of Detection Error Distance	45
Figure 25 Floors Detection Errors Using the Maximum Cell RSSI Algorithm when Power Fail in All Floor 2 Small Cells	46
Figure 26 Floors Detection Errors Using the Average Cells RSSI Algorithm when Power Fail in All Floor 2 Small Cells	47
Figure 27 Floor Detection Errors Using the Differential Average Cells RSSI Algorithm when Power Fail in All Floor 2 Small Cells.	48
Figure 28 CDFs of Floor Detection Errors Using the Maximum Cell RSSI, the Average Cells RSSI and the Differential Average Cells RSSI Algorithms when Power Fail in All Floor 2 Small Cells	49
Figure 29 Floor Detection Errors Using the Maximum Cell RSSI Algorithm when Power Fail in All Floor 1 Small Cells	50
Figure 30 Floor Detection Errors Using the Average Cells RSSI Algorithm when Power Fail in All Floor 1 Small Cells	51
Figure 31Floor Detection Errors Using the Differential Average Cells RSSI Algorithm when Power Fail in All Floor 1 Small Cells	51
Figure 32 Comparison of Floor Detection Errors when the Maximum Cell RSSI, the Average Cells RSSI and the Differential Average Cells RSSI Algorithms Are Exploited if Power Fail in All Floor 1 Small Cells.	52
Figure 33Floor Detection Errors Using the Differential Average Cells RSSI Algorithm when Power Fail in Small Cells in Different Floors	54
Figure 34 Vehicle Collision Avoidance Methodologies	60
Figure 35 Illustration of Doppler effect	62
Figure 36 Relative Phase Angles between Two Vehicles	63
Figure 37 Doppler Effects for Vehicles Traveling in Same or Different lane	64
Figure 38 Lanes Detection Methodology	64
Figure 39 Lanes Detection Scenario	66
Figure 40 Simulation Result of Lane Detection Probability with Different cvs	67
Figure 41 Decisions to Determine a Vehicle is in the Front or Back	69
Figure 42 Dangerous Index 1 and 2 Vehicles Detection Methodologies	71
Figure 43 Vehicles Dangerous Indexes Detection Scenario	72
Figure 44 Probabilities of Detected Vehicles Dangerous Indexes	73
Figure 45 Collision probabilities of Dangerous Index 1 and 2 in 1second Simulation Time	74
Figure 46 Highway Scenario	76
Figure 47 Variation of Vehicles Doppler Shift versus Time	79
Figure 48 Slopes of Vehicles Doppler Shifts	79
Figure 49 Probability of Other Vehicles in the Same Lane as Vehicle 2 (Object Vehicle) as Time Varies	80
Figure 50 Collision Probabilities between Other Vehicles with Vehicle 2 (Object Vehicle) as Time Varies	80
Figure 51 Number of Collisions between Other Vehicles with Vehicle 2 (Object Vehicle) as Time Varies	81
Figure 52 Scenario of Large Indoor Factory	83
Figure 53 3D UAV Positioning	83
Figure 54 Using Macro Cell to Enhance Indoor Floor Detection	84
Figure 55 Multiple Vehicles at a Crossroad or Intersection	86
Figure 56 Scenario between a Vehicle with Multi-Antenna and a Vehicle with One Antenna	86

LIST OF TABLES
Table 1 Overview of Comparisons between Various Positioning Systems [8]	4
Table 2 High Accuracy Positioning Requirements for Next Generation System	7
Table 3 Points Information	24
Table 4 Link Budget Parameters	35
Table 5 Indoor Scenario and Deployment Parameters	37
Table 6 Statistical Characteristics of Floor Detection Errors Using the Maximum Cell RSSI Algorithm and the Average Cells RSSI Algorithm	42
Table 7 Statistical Characteristics of Floor Detection Error Distance Using the Maximum Cell RSSI, the Average Cells RSI and the Differential Average Cells RSSI Algorithms when Power Fail in All Floor 2 Small Cells	49
Table 8 Number of Floor Detection Errors when the Maximum Cell RSSI, the Average Cells RSSI and the Differential Average Cells RSSI Algorithms Are Exploited when Power Fail in All Floor 1 Small Cells.	52
Table 9 Comparison of the Three Developed Floor Detection Algorithms with Other Available Algorithms in the Literatures	54
Table 10 Lane Detection Scenario Parameters	66
Table 11 Implication of Doppler Shift	69
Table 12 Parameters for Vehicles Dangerous Indexes Detection Scenario	72
Table 13 Dangerous Index vs. Vehicle Status	73
Table 14 Vehicles Doppler Shifts and Their Statuses	77

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