Multisensor Decision And Estimation Fusion

I Decision Fusion.- 1. Introduction.- 1.1 Conventional Statistical Decision.- 1.1.1 Basic model of statistical decision.- 1.1.2 Hypothesis testing.- 1.2 Multisensor Statistical Decision Fusion Summary.- 1.2.1 Brief introduction to multisensor data fusion.- 1.2.2 Some basic issues.- 1.2.3 The previous studies of decision fusion.- 1.3 Three Conventional Single Sensor Decisions.- 1.3.1 Bayes decision.- 1.3.2 Neyman-Pearson decision.- 1.3.3 Sequential decision.- 2. Two Sensor Binary Decisions.- 2.1 Introduction.- 2.1.1 Problem formulation.- 2.1.2 The relationship of distributed and classical decisions.- 2.2 Optimal Sensor Rule of Bayes Decision.- 2.2.1 Fixed point type necessary condition.- 2.2.2 Existence of the optimal sensor rule.- 2.3 An Algorithm for Computing the Optimal Sensor Rule.- 2.3.1 Gauss-Seidel iterative algorithm.- 2.3.2 The finite convergence of the discretized algorithm.- 2.4 Relationships with Likelihood Ratio Sensor Rules.- 2.5 Numerical Examples.- 2.6 Randomized Fusion Rules.- 3. Multisensor Binary Decisions.- 3.1 The Formulation for Bayes Binary Decision Problem.- 3.2 Formulation of Fusion Rules via Polynomials of Sensor Rules.- 3.3 Fixed Point Type Necessary Condition for the Optimal Sensor Rules Given a Fusion Rule.- 3.4 The Finite Convergence of the Discretized Algorithm.- 3.5 The Optimal Fusion Rule and Some Interesting Properties.- 3.6 Numerical Examples of the Above Results.- 3.7 Optimal Sensor Rule of Neyman-Pearson Decision.- 3.7.1 Necessary condition.- 3.7.2 The algorithm to search for optimal sensor rules.- 3.7.3 Numerical examples.- 3.8 Sequential Decision Fusion Given Fusion Rule.- 3.8.1 Algorithm.- 3.8.2 Numerical example.- 4. Multisensor Multi-Hypothesis Network Decision.- 4.1 Elementary Network Structures.- 4.1.1 Parallel network.- 4.1.2 Tandem network and tree network.- 4.1.3 Hybrid (tree) network.- 4.2 Formulation of Fusion Rule via Polynomial of Sensor rules.- 4.3 Fixed Point Type Necessary Condition for Optimal Sensor Rules Given a Fusion Rule.- 4.4 Iterative Algorithm and Convergence.- 5. Optimal Fusion Rule and Design of Network Communication Structures.- 5.1 Optimal Fusion Rule Given Sensor Rules.- 5.1.1 Problem formulation.- 5.1.2 Computation of likelihood ratios.- 5.1.3 Locally optimal sensor rules with communications.- 5.1.4 Extensions to more general systems.- 5.1.5 Numerical examples.- 5.2 The Equivalent Classes of Fusion Rules.- 5.2.1 Preliminary definitions.- 5.2.2 Propositions.- 5.2.3 Applications of propositions.- 5.3 Unified Fusion Rule for Parallel Network.- 5.4 Unified Fusion Rule for Tandem and Tree Networks.- 5.5 Performance Comparison of Parallel and Tandem Networks.- 5.6 Numerical Examples.- 5.6.1 Three sensor system.- 5.6.2 Four sensor system.- 5.7 Optimization Design of Network Decision Systems.- 5.7.1 Selection of a network structure category.- 5.7.2 Allocation of sensors' positions and communication amounts.- II Estimation Fusion.- 6. Multisensor Point Estimation Fusion.- 6.1 Previous Main Results.- 6.2 Linear Minimum Variance Estimation Fusion.- 6.2.1 Formulation of the LMV fusion as an optimization problem.- 6.2.2 Optimal fusion weights.- 6.2.3 Efficiency of the LMV fusion.- 6.2.4 Extension to a more general model.- 6.2.5 Previous fusion formulae as special cases.- 6.2.6 Discussion.- 6.2.7 Recursive computation of error covariance.- 6.3 The Optimality of Kalman Filtering Fusion with Feedback.- 6.3.1 Problem formulation.- 6.3.2 Global optimality of the feedback filtering fusion.- 6.3.3 Local estimate errors.- 6.3.4 The advantage of the feedback.- 6.3.5 Extension to a hybrid filtering fusion.- 6.4 Fusion of the Forgetting Factor RLS Algorithm.- 6.4.1 Forgetting factor RLS algorithm.- 6.4.2 Two types of distributed EFRLS fusion methods.- 6.4.3 Simulations.- 7. Multisensor.- 7.1 Statistical Interval Estimation Fusion Using Sensor Statistics.- 7.1.1 Problem formulation.- 7.1.2 Optimal convex linear fusion.- 7.1.3 Computation of the optimal weights.- 7.1.4 Nearly optimal linear fusion.- 7.1.5 Numerical examples.- 7.1.6 Inverting a hypothesis testing.- 7.2 Interval Estimation Fusion Using Sensor Estimates.- 7.2.1 Outputs of sensors.- 7.2.2 Combination rule of sensor outputs.- 7.2.3 Optimization criteria.- 7.3 Fault-Tolerant Interval Estimation Fusion.- 7.3.1 Without knowledge of confidence degrees.- 7.3.2 With knowledge of confidence degrees.- 7.3.3 Extension to sensors outputting multiple intervals.- 7.3.4 Conclusion.