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系統識別號 U0026-0208201611253500
論文名稱(中文) 應用蜜蜂繁殖最佳化演算法於行動台位置估測
論文名稱(英文) Using Honey Bees-Mating Optimization Algorithm for Mobile Station Location Estimation
校院名稱 成功大學
系所名稱(中) 電腦與通信工程研究所
系所名稱(英) Institute of Computer & Communication
學年度 104
學期 2
出版年 105
研究生(中文) 陳資尹
研究生(英文) Tzu-Yin Chen
學號 Q36031368
學位類別 碩士
語文別 中文
論文頁數 55頁
口試委員 指導教授-郭文光
口試委員-陳見生
口試委員-梁耀仁
口試委員-李信杰
口試委員-熊大為
中文關鍵字 無線定位  非視線傳播效應  蜜蜂繁殖最佳化演算法  抵達時間定位法 
英文關鍵字 Wireless Localization system  non-line-of-sight propagation error  Honey Bees-Mating Optimization algorithm  Time of arrival. 
學科別分類
中文摘要 現今的科技日益進步,伴隨之無線通訊技術也突飛猛進地發展著,而無線定位技術也越來越被需要。無線定位技術除了軍事用途、商業用途之外亦有緊急救難用途,故提供一個穩定且快速的定位服務是必須的。但我們處在的環境中有太多的雜訊以及非視線傳播(non-line-of-sight, NLOS)的存在,而這將導致我們的定位服務產生誤差,因此減少這些雜訊即誤差便成為了無線定位系統中最重要的一項課題。
本文中我們為了改善定位的精準度,在此引進了蜜蜂繁殖最佳化演算法(Honey Bees-Mating Optimization, HBMO)之技術。其為參考蜜蜂群體的生殖、養育行為並加以改良來降低我們的雜訊與非視線傳播誤差,並幫助我們達到最佳化的功能。我們將量測從各個基地台到行動台的抵達時間定位法(Time of arrival, TOA)來計算各個基地台到行動台的相對距離,之後再利用我們的蜜蜂繁殖最佳化演算法,透過目標函數進行演算後達到降低NLOS誤差並提升定位之精準度。
英文摘要 With the science and technology progress day by day, the wireless communication technology is also developing by leaps and bounds. And also, the wireless localization technology is increasingly being required. Wireless localization technology in addition to military applications and commercial purposes also got emergency rescue purposes, it must provide a stable and fast positioning service. But the environment we usually stay in got a lot of noise and non-line-of-sight propagation error, which will lead us to the locate device to the wrong place. So to deal with this noise and error has become one of the most important issues in wireless Localization system.
In this paper we have to improve the positioning accuracy, where we introduce the Honey Bees-Mating Optimization algorithm into it. Which is a reference to reproductive bee populations, and parenting behavior. This can be improved to reduce our noise and non-line-of-sight propagation error, and help us to achieve the best function. We will measure the propagation time of the signal from the base station to the mobile station, and then we calculate the relative distance from the TOA value. After we got some information from TOA, we use the HBMO algorithm to optimization the result, which calculation by the objective function to reduce NLOS error and improve the accuracy of positioning.
論文目次 目錄
中文摘要 I
英文摘要 II
誌謝 V
目錄 VI
圖目錄 IX
表目錄 XI
第一章 序論與介紹 1
1.1定位系統的分類 2
1.1.1衛星定位(Satellite-Based Positioning) 2
1.1.2地面定位(Terrestrial-based positioning) 3
1.1.3混合型定位 3
1.2無線傳輸之誤差 3
1.2.1非視線誤差(Non-Line-of-Sight Propagation) 4
1.2.2多重路徑干擾(Multipath Propagation) 5
1.2.3適應性雜訊(Additive Noise) 7
1.2.4接收能力(Hearability) 7
第二章 行動定位方法 8
2.1基本的無線定位方法 8
2.1.1空間識別(Cell-Identification, Cell-ID) 8
2.1.2接收訊號強度值(Received Signal Strength Indication, RSSI) 9
2.1.3抵達角度定位法(Angle of Arrival, AOA) 10
2.1.4抵達時間定位法(Time of Arrival, TOA) 14
2.1.5抵達時間差定位法(Time Difference of Arrival, TDOA) 16
2.1.6混合地面定位方法(Hybrid Methods) 18
2.2較簡便的無線定位演算判斷法 19
2.2.1泰勒級數展開法(Taylor Series Algorithm, TSA) 19
2.2.2線性位置方法(Linear Lines of Position, LLOP) 20
2.2.3距離縮放演算法(Range Scaling Algorithm, RSA) 21
第三章 蜜蜂繁殖最佳化演算法 22
3.1蜜蜂習性簡介 22
3.1.1蜂群各成員之工作職責 22
蜂后 22
雄蜂 23
工蜂 23
3.1.2工蜂養育幼蜂的影響 24
3.2蜜蜂繁殖最佳化演算法模型之建立 24
3.2.1蜂后與雄蜂交配模型之建立 24
3.2.2蜂后產卵行為模型之建立 25
3.2.3改善工蜂的餵養方式機率 26
3.2.4工蜂養育幼蜂行為模型之建立 27
隨機漫步(Random Walk) 27
貪婪法(Greedy Algorithm) 27
登山算法(Hill climbing) 27
3.2.5最佳新世代蜂后替換舊蜂后 28
3.3待求解問題與演算法間之關聯性 28
3.4蜜蜂繁殖最佳化演算法的運作流程 29
第四章 根據演算法的定位方法 31
4.1誤差模型(Noise model) 31
4.1.1均勻分佈誤差模型(uniformly distributed noise model) 31
4.1.2圓盤縮放誤差模型(circular disk of scatters model) 32
4.2模擬方法 33
4.2.1 估測模型介紹 33
4.2.2 程式運作流程 39
第五章 模擬結果 41
第六章 結論 51
參考文獻 52

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