多感測器資料融合乃處理及協調來自其他感測器和一些設備來源的訊息以提供一個較佳融合結果。此廣泛應用於軍事及非軍事領域上。 本文針對全連結分散型網路(Fully Connected Decentralized Network,簡稱FCDN)之資料融合架構，結合卡曼濾波器提出模糊基因演算之技術而構成智慧型連結分散型網路(Intelligent Connected Decentralized Network,簡稱ICDN)，靈活分配訊息之傳輸以提高整合系統精度。首先，FCDN 之結構乃由許多節點（感測器）構成之網路，每個節點具備其獨立處理局部觀測量即與鄰近節點相互傳遞訊息之能力。而後藉由卡曼濾波器對於通訊連結之訊息作一局部估測。二者基於方差配合技術（Covariance matching technique）以調整測量噪音方差R所設計之模糊適應卡曼濾波器，同時考慮濾波器性能狀況設計模糊權重鑑別器。此結合二種機制之多感測側器架構，應用於追蹤探測、導航導引等之需求。最後，依上所述，使用GA扮演資料融合處理及補償模糊系統設計之重要角色。以追蹤飛行目標為例，ICDN架構比FCDN及傳統分散式(Classical Decentralized) CD架構於精度上有不錯的表現。

Multisensor data fusion is the processing and synergistic combination of information gathered from multiple sources and sensors to provide a better inference of phenomenon. It is being applied to the wide range applications including military and non-military fields. For tracking monitoring, an Intelligent Connected Decentralized Network (ICDN) data fusion architecture, which embeds Fuzzy-Genetic Algorithm (FG) and Kalman filtering technique, is explored. The overhead communication problem in FCDN will be resolved by ICDN. At first, describing the FCDN consists of nodes (sensors), each with its own processing facility, which takes local observations and shares information with other neighbor nodes. It then assimilates the communicated information and computes a local estimate using Kalman filter. Second, based on a covariance matching technique, fuzzy-adaptive Kalman filter and Weighted Fuzzy Assessor (WFA) is designed respectively in order to estimate the appropriate measurement noise covariance matrix R and assign a weight that indicates a degree of the filter performance. Finally, the Genetic Algorithm (GA) acts as an important role in ICDN architecture. It compensates the insufficiency of fuzzy logic system designed and overcomes the communication limitation of FCDN. The result shows good performance of ICDN rather than that FCDN. Even best than Classical Decentralized (CD) architecture whose sensor or node doesn’t transmit information with each other.

中文摘要 i
Abstract ii
Table of Contents iii
List of Figures v
List of Tables vii
Chapter 1 Introduction 1
1.1 Motivation 2
1.2 Background 3
1.3 Method and Procedure 4
1.4 Overview 5
Chapter 2 Data Fusion in Decentralized Network 7
2.1 Sensor Classification and Selection 8
2.1.1 The Fusion Architectures of Multisensor Systems 9
2.1.2 The Advantages of Decentralized System 12
2.1.3 A Outline of Decentralized Structures 13
2.1.4 The Limitations of A Fully Connected Decentralized Network 14
2.2 Decentralized Estimators 16
2.2.1 The Decentralized Kalman Filter (DKF) 16
2.2.2 The CKF and DKF Algorithm 17
2.3 The Application of Decentralized Data Fusion 23
Chapter 3 Intelligent Learning Methods 25
3.1 Fuzzy Logic 26
3.1.1 Adaptive Kalman Filter 27
3.1.2 Weighted Fuzzy Assessor 28
3.2 Genetic Algorithm 30
Chapter 4 A Fully Decentralized Network with Fuzzy-GA 33
4.1 The Structure of ICDN 35
4.2 An Illustrative Example 37
4.3 An Example of Tracking Object (1) 39
4.3.1 Fuzzy Logic System Design 41
4.3.2 Genetic Algorithm Design 43
4.4 Numerical experimental Results 44
4.5 Discussion 47
Chapter 5 Simulation and Discussion 48
5.1 An Illustrative Example (2) 48
5.2 An Example of Tracking Objective 49
5.2.1 Fuzzy Logic System Design 50
5.2.2 Genetic Algorithm Design 53
Chapter 6 Conclusions 60
References 62

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