蜂巢式行動通訊系統使用道路資訊協助之最大似然法則進行相關性遮蔽信號環境中之路徑追蹤研究

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研究生：	鍾欣家 Hsin-Chia Chung
論文名稱：	蜂巢式行動通訊系統使用道路資訊協助之最大似然法則進行相關性遮蔽信號環境中之路徑追蹤研究 Road Information Aided Maximum Likelihood Route Tracking for Mobile Cellular Systems in Correlated Shadowing Environments
指導教授：	蔡育仁 Yuh-Ren Tsai
口試委員:
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 通訊工程研究所 Communications Engineering
論文出版年：	2007
畢業學年度：	95
語文別：	英文
論文頁數：	74
中文關鍵詞：	相關性遮蔽信號、信號強度、最大似然法則、道路資訊
相關次數：	點閱：1 下載：0
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近日來，精確追蹤行動用戶端的位置在許多的應用方面已經受到越來越多的注意。在這篇論文中，我們提出了一個在無線通訊系統中，有關於追蹤行動用戶端移動位置的演算法，這個提案是建立在最大似然法的估計理論上。這篇論文的第一部份是討論有關考慮在相關性遮蔽信號環境中，採用多個不同基地台所接收到的信號強度做為量測資料，以最大似然估計理論的演算法來估計一段單一的行動用戶移動路徑。接著在第二部份，引進道路資訊的概念做為路徑追蹤的輔助資料，則可以偵測由許多片段組合而成的一條行動用戶端完整的移動路徑。關於這個追蹤行動用戶完整路徑的演算法，是由兩種不同的估計與檢測理論組合而成的來運作的：第一種是前文所提追蹤一段單一路徑的演算法，而另一種則是在引進相關的道路資訊後，在每一個道路的交叉路口對於不同的移動方向，使用最大似然假設檢定法則來追蹤行動用戶的位置。
藉由執行程式的模擬結果中可以觀察得到，如果用來估計行動用戶移動性的追蹤步數越多，或是採用更多基地台所收到信號強度的資料，亦即得到的觀察量越多；則對於行動用戶端移動路徑的追蹤將能得到更精確的估計結果。然而，雖然單一片段目標行動用戶移動路徑追蹤的演算法則，並沒辦法得到精確的偵測結果，但是當加入了道路資訊的輔助之後，在考慮相關性遮蔽信號環境的蜂巢式行動通訊系統中，則可以完美無誤的追蹤到行動用戶端一整段完整的移動路徑。

Recently, accurate location information of mobile stations (MSs) is desired for more and more applications. This thesis presents a proposal of a MS location tracking algorithm based on the maximum likelihood (ML) function in wireless cellular systems. The first part of this algorithm adopts measurements of uplink received signal strength (RSS) in a correlated shadowing environment to track a single path based on ML estimation. Aided with road information, the whole MS moving route, consisting of multiple moving paths, is then reconstructed by the mixed algorithm of the single path tracking combined with the detection of moving directions on road intersections by ML hypothesis testing.
The observations from the simulation results show that, if the larger measuring data of tracking-step is adopted or more measurements from different BSs are gathered, then the better tracking performance will be achieved. Although the single path tracking may not exactly trace each moving segment of a target, with the help of road information the whole route can still be perfectly detected without any loss in the correlated shadowing microcellular environments.

Abstract    1
Contents    2
Chapter 1  Introduction    4
Chapter 2  System and Propagation Models    7
1 System and Mobility Models    7
2 Path Loss Model    8
2.1 Path Loss and System Models    8
2.2 Linear Approximation for Path Loss    8
3 Correlated Shadowing Model    10
3 Signal Strength    12
4 Distribution of RSS difference    12
4.1 Distribution of Shadowing Difference    12
4.2 Difference of Signal Strength    14
4.3 Transform Signal Strength Difference  li (n)  into Linear Form    15
4.4. Distribution of RSS Difference    15
Chapter 3  Maximum Likelihood (ML) Functions    17
1 RSS-Based Maximum Likelihood Function    17
2 RSS Difference-Based Maximum Likelihood Function    20
Chapter 4  Path Tracking Based On Minimize Object Functions fRSS and fdiff With Respect To   and      22
1 Path Tracking via Known MS Moving Speed with the Measurements of RSS    22
1.1 Assumptions    22
1.2 Minimize   with Argument   by Lagrange Method    23
2 Path Tracking via Unknown MS Moving Speed with the Measurements of RSS    27
2.1 Assumptions    27
2.2 Differentiation of Covariance Matrix    28
2.3 Minimize   with Argument   by Karush-Kuhn-Tucker (KKT) Theorem    32
3 Path Tracking via Known MS Moving Speed with Measurements of RSS Difference    36
3.1 Assumptions    37
3.2 Minimize   with Argument   by Lagrange Method    37
Chapter 5  Road Information Aided MS Route Tracking    42
1 Corner Effect    42
2 Road Information Aided MS Route Estimation    46
2.1 Maximum Likelihood Function Corresponding to Corner Effect    46
2.2 Detection of MS Route in Microcellular Systems    46
Chapter 6  Simulation Results & Discussions    48
1 Simulation Settings    48
2 Performances of ML Path Tracking via Lagrange and KKT Methods    49
2.1 RSS-based ML Path Tracking with Known MS Speed via Lagrange Method    49
2.2 RSS-Difference-based ML Path Tracking with Known MS Speed via Lagrange Method    53
2.3 RSS-based ML Path Tracking with Unknown MS Speed via KKT Method    57
3 Performance of ML Path Tracking with Respect to MS Moving Direction θ    61
3.1 Occurrence Probability of RSS-based Lagrange    61
3.2 Estimated   Error for Different Tracking-step    64
3.3 Effects of Different Shadowing Standard Deviation    67
4 Performance of Road Information Aided ML Route Tracking    69
Chapter 7  Conclusions    71
Bibliography    72

                                

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