臺灣博碩士論文加值系統

近年來，分散式偵測已經被廣泛的研究。分散式偵測最普遍的模型是包含分散的本地感測
器及融合中心。在一個分散式偵測系統中，許多個感測器共同去區別兩個或者多個假設。
例如，有無目標存在。在這篇論文中，傳統的分散式偵測在無線感測器網路的應用下被重
新檢視。為了將融合中心的錯誤率最小化，我們考慮一個在通道感知系統下設計最佳二位
元本地感測器偵測法則的傳統方法。換言之，它結合傳輸通道特性去找到最佳二位元本地
感測器偵測門檻以將融合中心的錯誤率最小化。並且，在不同的通道狀態資訊下，會有不
同的最佳二位元本地感測器偵測門檻。由於最佳多位元(軟式)本地感測器偵測比最佳二位
元本地感測器偵測更實用。為了允許多位元本地感測器輸出，我們也考慮了另一個在通道
感知系統下設計最佳多位元本地感測器偵測法則的傳統方法。然而要設計最佳的本地感測
器偵測法則，傳統的兩個方法都很容易陷入局部最佳門檻，取決於預先選定的初始值。為
了克服這個問題，我們考慮了數種改良式模擬退火演算法。根據這些改良式模擬退火演算
法及兩種傳統方法，我們提出兩種新型模擬退火演算法以實現最佳本地感測器偵測法則。
電腦模擬結果顯示使用兩種新型模擬退火演算法在最佳二位元本地感測器偵測問題及最
佳多位元本地感測器偵測問題都可以避免陷入局部最佳門檻。並且兩種新型模擬退火演算
法比起傳統的模擬退火演算法提供較低的搜尋點數效能。

Recently, distributed detection has been intensively studied. The prevailing model for
distributed detection (DD) is a system involving both distributed local sensors and a fusion
center. In a DD system, multiple sensors work collaboratively to distinguish between two or
more hypotheses, e.g., the presence or absence of a target. In this thesis, the classical DD
problem is reexamined in the context of wireless sensor network applications. For minimize the
error probability at the fusion center, we consider the conventional method that designs the
optimal binary local sensor decision rule in a channel-aware system, i.e., it integrates the
transmission channel characteristics for find the optimal binary local sensor decision threshold
to minimize the error probability at the fusion center. And there have different optimal local
sensor decision thresholds for different channel state information. Because of optimal multi-bit
(soft) local sensor decision is more practical than optimal binary local sensor decision.
Allowing for multi-bit local sensor output, we also consider another conventional method that
designs the optimal multi-bit (soft) local sensor decision rule in a channel-aware system.
However, to design the optimal local sensor decision rule, both of two conventional methods
are easily trapped into local optimal thresholds, which are depended on the pre-selected
initialization values. To overcome this difficulty, we consider several modified Simulated
Annealing (SA) algorithms. Based on these modified SA algorithms and two conventional
methods, we propose two novel SA algorithms for implementing the optimal local sensor
decision rule. Computer simulation results show that the employments of two novel SA
algorithms can avoid trapping into local optimal thresholds in both optimal binary local sensor
decision problem and optimal multi-bit local sensor decision problem. And two novel SA
algorithms offer superior performance with lower search points compared to conventional SA
algorithm.

誌謝 ............................................................................................................................. .i
Abstract .................................................................................................................... .ii
Contents .................................................................................................................... iv
List of Figures ........................................................................................................ vi
List of Tables ........................................................................................................ viii
Chapter 1 Introduction......................................................................................1
Chapter 2 Model of the Distributed Detection System ..........................5
2.1 Introduction ....................................................................................................5
2.2 System Model Description .............................................................................6
2.3 LRT for Optimal Binary Local Sensor Decision .........................................8
2.4 LRT for Optimal Multi-Bit Local Sensor Decision ................................... 11
Chapter 3 Simulated Annealing Parameter Setting and Novel
Simulated Annealing Algorithms ............................................16
3.1 Introduction ..................................................................................................16
3.2 Initial Current Solution Setting of the SA Algorithms .............................19
3.2.1 The Diagonal Initial Current Solution Setting of the SA Algorithm .......20
3.2.2 Initial Current Solution Setting in the SA Algorithm with the Parallel
Simulated Annealing Algorithm ...............................................................21
v
3.3 Neighborhood Structure of the SA Algorithm ...........................................23
3.3.1 The Directional Neighborhood Structure of the SA Algorithm ...............23
3.4 Cooling Schedule ..........................................................................................26
3.4.1 Conventional Cooling Schedule ..............................................................26
3.4.2 The Modified Lam Schedule ....................................................................27
3.4.3 Parallel Temperature Cooling Schedule (PTCS) ....................................30
3.5 Novel Simulated Annealing Algorithms .....................................................31
Chapter 4 Computer Simulation Results ..................................................34
4.1 Preliminaries ................................................................................................34
4.2 Computer Simulation Results of Optimal Binary Local Sensor Decision
Rule................................................................................................................36
4.3 Computer Simulation Results of Optimal Multi-Bit Local Sensor
Decision Rule ................................................................................................42
Chapter 5 Conclusions .....................................................................................46
Appendix A Method of [4] for Optimal Binary Local Sensor
Decision ........................................................................................48
Appendix B Method of [6] for Optimal Multi-Bit Local Sensor
Decision ........................................................................................53
Appendix C The Transformation for Local Sensor Decision
Thresholds ...................................................................................63
Appendix D The SA-Based Approach [9] for Optimal Local Sensor
Decision Rule .............................................................................65
vi
References ................................................................................................................68

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