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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/53983

Title: 利用天線陣列室內定位與環境偵測之實現
Implementation of Indoor Location and Environment Detection via Antenna Array
Authors: Chou, Ting-Wei
周庭緯
Contributors: NTOU:Department of Communications Navigation and Control Engineering
國立臺灣海洋大學:通訊與導航工程學系
Keywords: Array Antenna;FFT;Beam Steering;DOA
Array Antenna;FFT;Beam Steering;DOA
Date: 2019
Issue Date: 2020-07-09T02:52:30Z
Abstract: 目前常用無線電波訊號室內定位的方法包括:無線電波訊號接收訊號強度索引值(Received Signal Strength Indicator, RSSI)與利用延遲剖面(Power Delay Profile)特徵比對進行定位。其中RSSI由於計算簡單,可快速估測使用者之位置,但此方法的缺點在於許多參數並無法精準估計,且上述參數會因不同時間有不同的統計特性,因此造成在室內定位上有相當的誤差;而利用延遲剖面特徵比對之方法需要於室內預先大量建置不同位置對應之延遲剖面資料庫才能精準定位出使用者位置。 此外許多應用中,例如AR、空間建模等等,都需要知道使用者與周遭環境的相對關係,因此本文提出室內定位與環境偵測系統,使用天線陣列來進行室內阻礙物估測,並以座標系統來顯示使用者與障礙物的相對位置。 為了提高室內定位與環境偵測系統之精準度,本文提出訊號來向估測法:FFT & Beam Steering與FFT & Sum & Difference Pattern,訊號延遲時間估測法:Beam Steering Estimated Delay Time與Average of Delay Time。 經過模擬與分析,訊號來向估測演算法:FFT & Beam steering與FFT & Sum & Difference Pattern確實能降低系統複雜度,並維持相同的估測精準度,而提出之訊號延遲時間估測法:Average of Delay Time,在相同頻寬下,可從延遲剖面得到更精準的延遲時間估測。此二方法皆能確實提高定位系統之精準度。 本文的室內定位與環境偵測系統,於RayTracing軟體模擬中阻礙物之定位誤差平均約為0.25 m,近乎可定位出阻礙物位置。於實際場景中阻礙物定位誤差約為0.4 m,雖比模擬場景之誤差多了約0.15 m,但所提方法的確能快速量測出該位置的阻礙物,並維持精準定位之優點。
Currently, the common indoor positioning methods accomplished by radio wave signals include Received Signal Strength Indicator (RSSI) and comparison of power delay profile method. Although RSSI can quickly estimate the users’ position because of its simple calculation, it has disadvantage of inaccurate estimation of many parameters. Furthermore, the parameter RSSI used have quite different statistical characteristics due to different times, which results in estimation error in indoor positioning. On the other hand, the comparison of power delay profile method needs to collect enough delay profiles of locations in the environment so that it can accurately locate the users’ position. In many applications, such as Augmented Reality (AR) and environment reconstruction, all of them have to know the relative position of users and environment. Therefore, this paper proposes a system that can perform indoor positioning and environment detecting. The system uses antenna array to detect the obstacles in the environment, and shows the relative position of users and obstacles in a coordinate system. In order to improve the accuracy of indoor positioning and environment detection system, this thesis proposes FFT & Beam Steering and FFT & Sum & Difference Pattern methods to estimate the direction of arrival; This thesis also proposes Beam Steering Estimated Delay Time and Average of Delay Time methods to estimate delay time of signals. The simulation results show that FFT & Beam Steering and FFT & Sum & Difference Pattern methods not only reduce the complexity of Beam Steering but also maintain the accuracy of estimation. In addition, proposed Average of Delay Time method can acquire more accurate estimation of delay time from delay profiles within same bandwidth. The two proposed methods can be cooperated to improve the accuracy of positioning system. The indoor positioning and environment detection system of this paper uses a commercial Ray Tracing software to simulate the frequency response of transceiver in the environment. The estimation error of obstacle position is 0.25 m in the simulation, which means that the estimated obstacle position is almost consistent with the actual position. In practical experiment scenario, the estimation error of obstacle position is 0.4 m. Even if there is an estimation error of 0.15 m compared with simulation result, the proposed method apparently does estimate the obstacle’s position in a short time, and maintain the accuracy of estimation.
URI: http://ethesys.lib.ntou.edu.tw/cgi-bin/gs32/gsweb.cgi?o=dstdcdr&s=G0010667009.id
http://ntour.ntou.edu.tw:8080/ir/handle/987654321/53983
Appears in Collections:[通訊與導航工程學系] 博碩士論文

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