Multisensor Decision And Estimation Fusion / Edition 1

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Overview

Useful as a reference and as a text in an advanced course, this book treats the fundamentals of multisensor decision and estimation fusion in order to deal with general random observations or observation noises that are correlated across the sensors. For multisensor decision fusion with general sensor observations given a fixed fusion rule, the book demonstrates a necessary condition for optimum sensor rules and presents fusion rules for some specific decisions systems. For the multisensor point estimation fusion problem, a general version of the linear unbiased minimum variance estimation fusion rule is developed. Some new interval estimation fusion methods are proposed. Zhu is affiliated with Sichuan University, China. Annotation (c)2003 Book News, Inc., Portland, OR
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Product Details

Table of Contents

List of Figures
List of Tables
Preface
Acknowledgments
1 Introduction 3
1.1 Conventional Statistical Decision 3
1.2 Multisensor Statistical Decision Fusion Summary 6
1.3 Three Conventional Single Sensor Decisions 11
2 Two Sensor Binary Decisions 37
2.1 Introduction 37
2.2 Optimal Sensor Rule of Bayes Decision 41
2.3 An Algorithm for Computing the Optimal Sensor Rule 48
2.4 Relationships with Likelihood Ratio Sensor Rules 53
2.5 Numerical Examples 55
2.6 Randomized Fusion Rules 60
3 Multisensor Binary Decisions 63
3.1 The Formulation for Bayes Binary Decision Problem 64
3.2 Formulation of Fusion Rules via Polynomials of Sensor Rules 65
3.3 Fixed Point Type Necessary Condition for the Optimal Sensor Rules Given a Fusion Rule 67
3.4 The Finite Convergence of the Discretized Algorithm 71
3.5 The Optimal Fusion and Some Interesting Properties 78
3.6 Numerical Examples of the Above Results 83
3.7 Optimal Sensor Rule of Neyman-Pearson Decision 88
3.8 Sequential Decision Fusion Given Fusion Rule 94
4 Multisensor Multi-Hypothesis Network Decision 101
4.1 Elementary Network Structures 101
4.2 Formulation of Fusion Rule via Polynomial of Sensor rules 106
4.3 Fixed Point Type Necessary Condition for Optimal Sensor Rules Given a Fusion Rule 110
4.4 Iterative Algorithm and Convergence 112
5 Optimal Fusion Rule and Design of Network Communication Structures 117
5.1 Optimal Fusion Rule Given Sensor Rules 117
5.2 The Equivalent Classes of Fusion Rules 134
5.3 Unified Fusion Rule for Parallel Network 140
5.4 Unified Fusion Rule for Tandem and Tree Networks 145
5.5 Performance Comparison of Parallel and Tandem Networks 146
5.6 Numerical Examples 148
5.7 Optimization Design of Network Decision Systems 153
6 Multisensor Point Estimation Fusion 159
6.1 Previous Main Results 160
6.2 Linear Minimum Variance Estimation Fusion 162
6.3 The Optimality of Kalman Filtering Fusion with Feedback 177
6.4 Fusion of the Forgetting Factor RLS Algorithm 184
7 Multisensor Interval Estimation Fusion 197
7.1 Statistical Interval Estimation Fusion Using Sensor Statistics 198
7.2 Interval Estimation Fusion Using Sensor Estimates 212
7.3 Fault-Tolerant Interval Estimation Fusion 219
Index 235
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