Multisensor Decision and Estimation Fusion
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...
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List of FiguresList of TablesPrefaceAcknowledgments1Introduction31.1Conventional Statistical Decision31.2Multisensor Statistical Decision Fusion Summary61.3Three Conventional Single Sensor Decisions112Two Sensor Binary Decisions372.1Introduction372.2Optimal Sensor Rule of Bayes Decision412.3An Algorithm for Computing the Optimal Sensor Rule482.4Relationships with Likelihood Ratio Sensor Rules532.5Numerical Examples552.6Randomized Fusion Rules603Multisensor Binary Decisions633.1The Formulation for Bayes Binary Decision Problem643.2Formulation of Fusion Rules via Polynomials of Sensor Rules653.3Fixed Point Type Necessary Condition for the Optimal Sensor Rules Given a Fusion Rule673.4The Finite Convergence of the Discretized Algorithm713.5The Optimal Fusion and Some Interesting Properties783.6Numerical Examples of the Above Results833.7Optimal Sensor Rule of Neyman-Pearson Decision883.8Sequential Decision Fusion Given Fusion Rule944Multisensor Multi-Hypothesis Network Decision1014.1Elementary Network Structures1014.2Formulation of Fusion Rule via Polynomial of Sensor rules1064.3Fixed Point Type Necessary Condition for Optimal Sensor Rules Given a Fusion Rule1104.4Iterative Algorithm and Convergence1125Optimal Fusion Rule and Design of Network Communication Structures1175.1Optimal Fusion Rule Given Sensor Rules1175.2The Equivalent Classes of Fusion Rules1345.3Unified Fusion Rule for Parallel Network1405.4Unified Fusion Rule for Tandem and Tree Networks1455.5Performance Comparison of Parallel and Tandem Networks1465.6Numerical Examples1485.7Optimization Design of Network Decision Systems1536Multisensor Point Estimation Fusion1596.1Previous Main Results1606.2Linear Minimum Variance Estimation Fusion1626.3The Optimality of Kalman Filtering Fusion with Feedback1776.4Fusion of the Forgetting Factor RLS Algorithm1847Multisensor Interval Estimation Fusion1977.1Statistical Interval Estimation Fusion Using Sensor Statistics1987.2Interval Estimation Fusion Using Sensor Estimates2127.3Fault-Tolerant Interval Estimation Fusion219Index235
