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Multisensor Decision and Estimation Fusion »

Book cover image of Multisensor Decision and Estimation Fusion by Yunmin Zhu

Authors: Yunmin Zhu
ISBN-13: 9781402072581, ISBN-10: 1402072589
Format: Hardcover
Publisher: Springer-Verlag New York, LLC
Date Published: November 2007
Edition: (Non-applicable)

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Author Biography: Yunmin Zhu

Book Synopsis

Table of Contents

List of Figures
List of Tables
1.1Conventional Statistical Decision3
1.2Multisensor Statistical Decision Fusion Summary6
1.3Three Conventional Single Sensor Decisions11
2Two Sensor Binary Decisions37
2.2Optimal Sensor Rule of Bayes Decision41
2.3An Algorithm for Computing the Optimal Sensor Rule48
2.4Relationships with Likelihood Ratio Sensor Rules53
2.5Numerical Examples55
2.6Randomized Fusion Rules60
3Multisensor Binary Decisions63
3.1The Formulation for Bayes Binary Decision Problem64
3.2Formulation of Fusion Rules via Polynomials of Sensor Rules65
3.3Fixed Point Type Necessary Condition for the Optimal Sensor Rules Given a Fusion Rule67
3.4The Finite Convergence of the Discretized Algorithm71
3.5The Optimal Fusion and Some Interesting Properties78
3.6Numerical Examples of the Above Results83
3.7Optimal Sensor Rule of Neyman-Pearson Decision88
3.8Sequential Decision Fusion Given Fusion Rule94
4Multisensor Multi-Hypothesis Network Decision101
4.1Elementary Network Structures101
4.2Formulation of Fusion Rule via Polynomial of Sensor rules106
4.3Fixed Point Type Necessary Condition for Optimal Sensor Rules Given a Fusion Rule110
4.4Iterative Algorithm and Convergence112
5Optimal Fusion Rule and Design of Network Communication Structures117
5.1Optimal Fusion Rule Given Sensor Rules117
5.2The Equivalent Classes of Fusion Rules134
5.3Unified Fusion Rule for Parallel Network140
5.4Unified Fusion Rule for Tandem and Tree Networks145
5.5Performance Comparison of Parallel and Tandem Networks146
5.6Numerical Examples148
5.7Optimization Design of Network Decision Systems153
6Multisensor Point Estimation Fusion159
6.1Previous Main Results160
6.2Linear Minimum Variance Estimation Fusion162
6.3The Optimality of Kalman Filtering Fusion with Feedback177
6.4Fusion of the Forgetting Factor RLS Algorithm184
7Multisensor Interval Estimation Fusion197
7.1Statistical Interval Estimation Fusion Using Sensor Statistics198
7.2Interval Estimation Fusion Using Sensor Estimates212
7.3Fault-Tolerant Interval Estimation Fusion219




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