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研究生:陳澤豪
研究生(外文):Ze-Hao Chen
論文名稱:應用模糊預測方法於感測網路之室內定位系統研究
論文名稱(外文):An Application of Fuzzy Forecasting Algorithms to Indoor Positioning Systems for Sensor Networks
指導教授:李俊賢李俊賢引用關係
口試委員:李岳峰許佳興高立人
口試日期:2012-07-31
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:電機工程系研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:65
中文關鍵詞:室內定位模糊推論類神經網路非直視路徑效應直線定位法
外文關鍵詞:indoor positioningline of positionfuzzy logic systemnon-line of sightNeural Network
相關次數:
  • 被引用被引用:4
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
在現代科技的發達下,很多應用被發展出來了,在這些應用之中定位系統也是發展相當迅速的技術之一。全球定位系統 (Global Position System, GPS)是現今最常見的定位技術,GPS是用衛星訊號來對欲追蹤對象做定位與導航,雖然GPS應用廣泛但由於衛星訊號會被建築物所遮蔽,所以當GPS套用在室內定位時存在定位精度不足的問題,所以必須透過其他定位感測技術來解決室內定位問題。常用的室內定位感測技術主要有紅外線、超聲波和無線電波技術,這些感測技術各有其物理特性與限制。本論文即是針對室內定位系統,以模糊理論為基礎融入位置預測系統,進而提高室內定位的準確度。
本研究是應用將ZigBee中無線電波的接收訊號強度轉換成距離,以達到三點定位之方式。由於在室內定位中存在著定位精度不足的問題,除了在測量上本身的誤差之外,non-line of sight (NLOS)也是造成定位精度不足的因素。其是在傳送訊號時遭到阻礙所產生的誤差,本論文就是針對此誤差,以模糊理論為基礎來實作位置預測系統,進而抑制室內定位的非直視性誤差,以達到更精準的定位。本論文所提出的系統主要是透過line of position (LOP )方法計算測量距離以得到移動節點的位置,再將得到位置的座標代入類神經網路進行訓練,進而減少測量時的誤差。另一方面記錄移動節點位置,帶入以模糊理論為基礎,並配合老人行走速度進行運算的位置預測系統,來得到下一時間點移動節點的位置。實驗的模擬結果顯現本研究,所提出的方法,有效地降低測量上與NLOS的誤差。


GPS is one of the most common positioning system technologies in locating and navigating object. However, the main disadvantage of GPS is that the satellite signals may be easily blocked by buildings. This condition may result in the low positioning accuracy when the GPS is applied in indoor positioning. For this reason, other positioning technology such as infrared, ultrasonic, or radio technology with its unique physical characteristics has been employed to solve the problems encountered in indoor positioning. This paper focuses on implementing the fuzzy logic system to forecast the location of an object and combining the neural network to improve the accuracy of indoor positioning.
In short, the low positioning accuracy in indoor positioning may be caused by instrumental errors, especially the effects by non-line of sight (NLOS). Essentially, NLOS results in errors when the signal is obscured during signal transmission. The purpose of this research is utilizing the forecast location algorithm based on the fuzzy logic system to reduce the NLOS error in indoor positioning.
Furthermore, the procedure of this method is designed in the following steps. First, the line of position (LOP) algorithm is used to calculate the position of mobile node. Then, the coordinate of position calculated by LOP is incorporated into neural network to reduce positional errors. In addition to achieve forecast location algorithm, the various walking speeds of an elder is integrated into the fuzzy logic system to estimate the coordinate of mobile node on the next time. The simulation results indicated that the instrumental and NLOS errors were significantly reduced.

中文摘要...................................................i
英文摘要.......................................................iii
誌謝.........................................................v
表目錄......................................................viii
圖目錄........................................................ix
第一章 緒論...............................................1
1.1 研究背景.........................................1
1.2 研究動機與目的...................................1
1.3 文獻回顧.........................................2
1.4 研究方法.........................................3
1.5 研究貢獻.........................................4
第二章 室內定位原理概述與ZigBee介紹.....................5
2.1 ZigBee介紹 ......................................5
2.1.1 ZigBee無線網路介紹..............................5
2.1.2 ZigBee標準發展與技術特性........................6
2.2 測距原理介紹....................................10
2.2.1 到達時間測距法.................................10
2.2.2 到達時間差測距法則.............................11
2.2.3 接收訊號角度測距法則...........................11
2.2.4 接收訊號強度測距法則...........................12
2.3 室內定位演算法..................................14
2.3.1 三邊定位法演算法...............................14
2.3.2 直線定位演算法.................................16
2.4 直視路徑介紹及改善方式..........................19
2.4.1 非直視路徑效應介紹與其特性.....................19
2.4.2 改良式機率類神經網路...........................20
2.4.3 線性化線位置...................................23
2.4.4 混合式定位法...................................25
第三章 系統架構與研究方法..............................29
3.1 系統架構........................................29
3.2 類神經網路......................................30
3.3 模糊預測位置演算法..............................34
3.4 NFLOP演算法.....................................46
第四章 模擬結果與分析..................................49
4.1 模擬環境設定....................................49
4.2 直線定位演算法模擬與分析........................51
4.3 HTA定位法模擬與分析.............................52
4.4 線性化直線定位法模擬與分析......................52
4.5 倒傳遞類神經網路模擬與分析......................53
4.6 模糊預測演算法模擬與分析........................54
4.7 NFLOP模擬結果與分析.............................55
第五章 結論與未來展望..................................62
參考文獻..................................................63

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