臺灣博碩士論文加值系統

從環境中蒐集資料的感測器網路讓許智慧型裝置，例如機器人、智慧型車輛甚致是生物
醫療器材的應用與設置成為可行的技術。我們觀察到傳統的方法分開執行感測器網路的訊息
融合、決策、與接下來的控制行動，而我們提出了一個創新的智慧型決策架構來做整個這些
裝置的系統之模型，而可以更進一步的增進系統效能來超越傳統方法。智慧型決策架構藉由
分開事件到觀察的映射，成為兩個映射，分別是從事件到物理量及從物理量到觀測，而改善
了傳統估計方法。數學公式化在本篇論文中建構出來而且應用於救火機器人的場景來展示它
的有效性。我們還更展示了智慧型決策架構在特定的條件下可以被退化成傳統的決策方法。
更重要的，我們可以把這個架構延展而超出傳統機制，到融合多個物理量的觀察然後獲得最
佳解條件。對於有限物理量相關性資訊下的決策，我們提出了觀察選擇然後求得其與最佳決
策等效之條件。較缺乏嚴謹數學架構的模糊邏輯常被應用於這樣的決策，而我們可以展示具
嚴謹定義的決策理論數學架構之觀察選擇可以退化成多觀察模糊邏輯決策。最後，模擬結果
顯示我們提出的智慧型決策架構的確改善了決策精準程度然後也增進了系統效能。除了感測
器網路，這個架構也可以應用於各種不同的智慧型或感知系統。我們提出了在智慧型決策架
構下發展出來的雙向時間分割頻譜偵測來展示除了感測器網路之外的應用。這個方法藉由僅
一個點的從獨立感測通道的多重觀察減低了隱藏點問題，而合作頻譜偵測則需要多重點去進
行多重觀察。這個方法更進一步的利用了因為地理位置間隔產生的路徑損失之資訊來增進感
測效能。分析及模擬結果顯示我們提出的頻譜偵測方法顯著的改善了傳統的頻譜偵測效能。

Sensor networks to collect various information from environments enable deployment and
application of many intelligent devices and systems, such as robots, intelligent vehicles, and even
biomedical instruments. Observing traditional approach separately executing information fusion
from sensor networks, decision, and later control functions, we propose a novel intelligent decision
framework to allow thorough system modeling of such devices, and thus further enhancement
beyond traditional approach. Intelligent decision framework improves traditional estimation theory
by separating the mapping from event to observation into two mappings, the mapping from
observed physical quantity to sensor observation and the mapping from target event to physical
quantity. The mathematical formulation is constructed and applied in the firefighting robot
navigation scenario to illustrate its effectiveness. We further shows that the intelligent decision
framework can be degenerated to traditional decision schemes under special conditions. More
importantly, we can extend the framework to fuse observations from multiple kinds of physical
quantities and derive the optimal decision, beyond traditional statistical decision mechanisms. For
the decision with limited knowledge of the correlations among physical quantities, we propose
Observation Selection and derive the equality condition with optimal decision. While fuzzy logic of
less strict-sense mathematic structure is commonly employed to resolve this application scenario,
we can demonstrate that Observation Selection derived from well-defined decision theory can be degenerated to fuzzy logic of multiple kinds of observations. Finally, simulation results show that
the proposed intelligent decision framework indeed improves the accuracy of the decision and
enhances system performance. In addition to sensor network, this framework can also be applied in
various intelligent system or cognitive systems. We propose a novel cognitive radio spectrum
sensing scheme, Dual-way Time-Division Spectrum Sensing, derived under intelligent decision
framework to demonstrate the application of this general framework other than sensor network.
This scheme mitigates the hidden terminal problem by only one node taking multiple observations
from independent sensing channel, while cooperative spectrum sensing needs multiple nodes to
perform multiple observation. Moreover, this scheme takes the path-loss due to geographical
separation into consideration to improve the sensing performance. Analytical and simulation result
shows that the proposed spectrum sensing scheme significantly improves the performance of
traditional spectrum sensing.
Keywords: Sensor network, information

誌謝……………………………………………………………………. ..I
中文摘要………………………………………………………………..II
英文摘要………………………………………………………………. IV
List of Figures…………………………………………………………IX
List of Tables…………………………………………………..………..XI
Chapter 1 Introduction………………………………………...............….1
1.1 Information Fusion……………………....................................................1
1.2 Sensor Network Based Intelligent System.......................................................6
1.3 Organization….................................................................................................8
Chapter 2 Intelligent Decision Framework..............................................10
2.1 Framework Overview………………….......................................................10
2.2 System Model…….……………………......................................................12
Chapter 3 Sensor Network Navigation System for Firefighting Robot…18
3.1 Intelligent Decision Framework for Firefighting Robot………………….…19
3.2 Sensor Observation Model……............………………………………….…20
3.3 Degenerate Problem: State space model…………………………………...23
Appendix 3. State-space Model with Estimation of Previous State…...………..27
Chap t e r 4 I n t e l l i g e n t D e c i s i on Framework- Multip l e
Observation……………………………………………………………31
4.1 Optimal Multi-Observation Decision System Model…………………….…32
4.2 Observation Selection...…………………………………………………….35
4.3 Cramer-Rao bound…………...……………………………………………..41
4.4 Optimal Ratio Combining…………………………………………………44
4.5 Fuzzy logic………………………………………………………………….46
4.6 Performance Comparison of Observation Selection and Ratio Combining...51
Chapter 5 Multi-Observation Sensor Network Navigation System for
Firefighting Robot....................................................................................61
5.1 Multi-Observation Intelligent Decision System Model………………….…61
5.2 Degenerate Problem: Fuzzy Logic Controller...………………………….…62
Chapter 6 Experiments………………………………………………….64
6.1 Single Observation……………………………………………………….…64
6.2 Multiple Observation……………………………………………………..…69
Chapter 7 Cognitive Radio Spectrum Sensing under Intelligent Decision
Framework………………………………………………………………74
7.1 Cognitive Radio Spectrum Sensing.…..………………………………….…74
7.2 Spectrum Sensing Model…………………….…………………………..…78
7.3 Spectrum Sensing Procedure and Algorithm…………………………..…81
7.4 Performance Analysis and Comparison……...…………………………..…86
7.5 Numerical Result……………………...……...…………………………..…90
Chapter 8 Conclusions and Future Works…………..…………………..97
Bibliography…………………………………………………………...99

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