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研究生:洪忠敬
研究生(外文):Chung-Ching Hung
論文名稱:非視線傳播環境利用基因演算法和叢簇技術之行動定位預測
論文名稱(外文):Mobile Location Estimation Using Genetic Algorithm and Clustering Technique for NLOS Environments
指導教授:林葭華
指導教授(外文):Cha-Hwa Lin
學位類別:碩士
校院名稱:國立中山大學
系所名稱:資訊工程學系研究所
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:2007
畢業學年度:95
語文別:英文
論文頁數:90
中文關鍵詞:非視線傳播基因演算法基於密度之叢簇演算法
外文關鍵詞:Non-line-of-sight (NLOS)Genetic algorithmDensity-based clustering algorithm (DCA)
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為因應個人化安全服務之需求,對於追蹤、監督、或緊急救難等之行動通訊定位技術,廣受政府、學術界、與業界所矚目,而即時提供最佳化辨識使用者所在位置之服務,更成為無線通訊領域中積極研究的主題之一。然而,現有的定位技術尚未能滿足低成本與良好的準確度之要求,我們認為針對目前的GSM無線通訊系統,設計新的行動定位演算法,將可有效改善使用者的滿意度。本研究將整合現有的GSM無線通訊系統中可利用的參考資訊,如行動台和基地台的幾何相對位置,及相關的定位技術,建立符合GSM行動電話規格的模型系統,進行實驗操作,探索基地台的相對位置和所涵蓋的通訊空間的交集區域,並且使用基於密度之叢簇演算法和基因演算法分析這通訊空間的交集區域,計算行動電話最有可能出現的位置。模擬實驗的結果顯示,本方法定位誤差在67%的情況下小於0.075km,而95%的情況下小於0.15km,定位精準度滿足E-911的要求。
For the mass demands of personalized security services, such as tracking, supervision, and emergent rescue, the location technologies of mobile communication have drawn much attention of the governments, academia, and industries around the world. However, existing location methods cannot satisfy the requirements of low cost and high accuracy. We hypothesized that a new mobile location algorithm based on the current GSM system will effectively improve user satisfaction. In this study, a prototype system will be developed, implemented, and experimented by integrating the useful information such as the geometry of the cell layout, and the related mobile positioning technologies. The intersection of the regions formed by the communication space of the base stations will be explored. Furthermore, the density-based clustering algorithm (DCA) and GA-based algorithm will be designed to analyze the intersection region and estimate the most possible location of a mobile phone. Simulation results show that the location error of the GA-based is less than 0.075 km for 67% of the time, and less than 0.15 km for 95% of the time. The results of the experiments satisfy the location accuracy demand of E-911.
Contents
CHAPTER 1 INTRODUCTION........................................1
CHAPTER 2 RELATED RESEARCHES.......................7
2.1 Location Solutions................................................8
2.1.1 Cell Identification (Cell-ID) Method............ .....8
2.1.2 Angle of Arrival (AOA) Method ..........................9
2.1.3 Time of Arrival (TOA) Method..........................10
2.1.4 Time Difference of Arrival (TDOA) Method ...12
2.1.5 Signal Strength (SS) Method..........................14
2.2 Non Line of Sight Propagation.........................15
2.2.1 Modeling the NLOS Error................................16
2.2.2 Circular Disk of Scatterers Model (CDSM)..17
2.3 TOA-Based Algorithms for NLOS Mitigation..18
2.3.1 Linear Lines of Position Algorithm (LLOP)..19
2.3.2 Range Scaling Algorithm (RSA).....................21
2.3.3 Density-Based Clustering Algorithm (DCA).22
2.4 Genetic Algorithm................................................23
2.4.1 Genetic Algorithm Process.............................23
2.4.2 The Fundamental Structure of GA.................25
CHAPTER 3 MOBILE LOCATION ESTIMATION USING GENETIC ALGORITHM....................................................34
3.1 Initial Population.................................................39
3.1.1 Caculation of Candidate Positions...............39
3.1.2 Constraint on Range Scaling Parameters..42
3.2 The Objective Function......................................46
3.2.1 The Fitness Funciton.......................................49
3.3 Selection and Reproduction.............................51
3.4 Encoding and Decoding....................................51
3.5 Adaptive Genetic Algorithm...............................52
3.6 Crossover Operation..........................................53
3.7 Constraint Operation..........................................54
3.8 Elitist Strategy......................................................54
3.9 Extinction and Immigration Strategy................55
3.10 The Convergence of GA..................................55
CHAPTER 4 SIMULATION DESIGN..............................57
4.1 Simulation Parameters.....................................58
4.2 Simulation Procedure.......................................60
CHAPTER 5 SIMULATION RESULTS AND DICUSSIONS....................................................................66
5.1 Effect of the NLOS Distribution ........................67
5.2 Effect of the Number of NLOS BSs.................69
5.3 Effect of the Magnitude of NLOS Error............71
5.4 Effect of the Percentage of Candidate Positions......................................................................72
CHAPTER 6 CONCLUSIONS........................................73
REFERENCES...................................................................74
APPENDIX .........................................................................77
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