隨著網路技術的快速發展和定位資訊需求的日益增加，定位問題越來越成為人們關注的焦點。1996年美國通訊委員會 (Federal Communications Commission) 在Enhanced 911 (E-911) 標準中要求無線通信網路業者必須提供手機用戶位置估計的功能，因此行動定位領域，已經引起了越來越多研究者的興趣。然而在無線通訊系統中，精確定位面臨的主要的問題之一是訊號非視線傳播 (NLOS) 造成的定位誤差。
本文提出在非視線傳播環境下能夠有效降低非視線傳播誤差的定位演算法。首先以抵達時間測量距離的基礎上，利用基地台和測量值之間的幾何關係及費瑪點定理，計算行動台所有可能的位置；然後根據可能位置的幾何分佈，利用叢簇技術產生一組加權值，可將測量距離縮減到最接近視線傳播 (LOS) 距離，並且產生新的目的函式，計算其最佳解，以預測行動台之位置。模擬並實驗此演算法在不同非視線傳播 (NLOS) 誤差模式下的定位準確性，實驗結果顯示本研究提出的演算法比傳統 Linear lines of position 定位演算法， Range scaling定位演算法有明顯提高定位準確性，且略微優於 Density-Based Clustering 定位演算法。演算法在不同非視線傳播誤差模式下有較佳且穩定的定位成效，其定位準確性也符合Enhanced 911 (E-911) 需求。

The technique of mobile location has become a popular research topic since the number of related applications for the location information is growing rapidly. The decision to make the location of mobile phones under the U.S. Federal Communications Commission (FCC) in 1996 is one of the driving forces to research and provide solutions to it. But, in wireless communication systems, non line of sight (NLOS) propagation is a key and difficult issue to improve mobile location estimation.
We propose an efficient location algorithm which can mitigate the influence of NLOS error. First, based on the geometric relationship between known positions of the base stations, the theorem of “Fermat Point” is utilized to collect the candidate positions (CPs) of the mobile station. Then, a set of weighting parameters are computed using a density-based clustering method. Finally, the location of mobile station is estimated by solving the optimal solution of the weighted objective function.
Different distributions of NLOS error models are used to evaluate the performance of this method. Simulation results show that the performance of the least range measure (LRM) algorithm is slightly better than density-based clustering algorithm (DCA), and superior to the range based linear lines of position algorithm (LLOP) and range scaling algorithm (RSA) on location accuracy under different NLOS environments. The simulation results also satisfy the location accuracy demand of Enhanced 911 (E-911).

CHAPTER 1 INTRODUCTION.................................1
CHAPTER 2 RELATED RESEARCHES...........................7
2.1 Location Methods..................................8
2.1.1 Cell Identification (Cell-ID) Method...........9
2.1.2 Angle of Arrival (AOA) Method.................10
2.1.3 Time of Arrival (TOA) Method..................11
2.1.4 Time Difference of Arrival (TDOA) Method......13
2.1.5 Signal Strength (SS) Method...................14
2.1.6 Global Positioning System (GPS)...............16
2.2 Non Line of Sight Propagation....................19
2.2.1 NLOS Identification and Mitigation............19
2.3 TOA-Based Algorithms for NLOS Mitigation.........21
2.3.1 Linear Lines of Position Algorithm (LLOP).....22
2.3.2 Range Scaling Algorithm (RSA).................23
2.3.3 Density-Based Clustering Algorithm (DCA)......24
2.4 Modeling the NLOS Error..........................27
2.4.1 Circular Disk of Scatterers Model (CDSM)......29
2.5 The Fermat Point of a Triangle...................30
2.5.1 Definition of the Fermat Point................30
2.5.2 An Example to Locate the Fermat Point.........33
CHAPTER 3 LEAST RANGE MEASURE ALGORITHM...............36
3.1 Calculation of the Candidate Positions...........39
3.2 Constraint on Range Scaling Parameters...........43
3.3 Modification of the Objective Function...........47
3.4 Location Estimation of the Mobile Station........50
CHAPTER 4 SIMULATION DESIGN...........................52
4.1 Simulation Parameters............................53
4.2 Simulation Procedure.............................55
CHAPTER 5 SIMULATION RESULTS AND DICUSSIONS...........61
5.1 Effect of the NLOS Distribution..................62
5.2 Effect of the Number of NLOS BSs.................64
5.3 Effect of the Magnitude of NLOS Error............66
5.4 Effect of the Percentage of Candidate Positions..67
CHAPTER 6 CONCLUSIONS.................................69
REFERENCES...............................................70

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