淡江大學機構典藏:Item 987654321/111431
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    題名: Study on higher accuracy positioning for 5G mobile communication networks
    其他題名: 5G行動通訊網路下之高精準度定位研究
    作者: 羅智元;Lo, Chih-Yuan
    貢獻者: 淡江大學電機工程學系博士班
    李揚漢
    關鍵詞: 5G;高精準定位;三維定位;快速定位;大數量定位;權重定位;樓層偵測;車道偵測;High Accuracy Positioning;3D Positioning;Fast Positioning;Large Amount of Data Positioning;Weighing-Factor Positioning;Floor Detection;Vehicle Lane Detection
    日期: 2016
    上傳時間: 2017-08-24 23:52:42 (UTC+8)
    摘要: 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.
    顯示於類別:[電機工程學系暨研究所] 學位論文

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