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BL
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| Record Number
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751465
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b571424
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| Main Entry
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Yunmin Zhu [und weitere].
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| Title & Author
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Networked multisensor decision and estimation fusion : : based on advanced mathematical methods\ Yunmin Zhu [und weitere].
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| Publication Statement
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Boca Raton, Fla : CRC Press, 2013
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| ISBN
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1439874522
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: 1439874530
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: 9781439874523
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: 9781439874530
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| Contents
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IntroductionFundamental Problems Core of Fundamental Theory and General Mathematical Ideas Classical Statistical Decision Bayes Decision Neyman-Pearson Decision Neyman-Pearson Criterion Minimax Decision Linear Estimation and Kalman Filtering Basics of Convex Optimization Convex Optimization Basic Terminology of Optimization Duality Relaxation S-Procedure Relaxation SDP Relaxation Parallel Statistical Binary Decision Fusion Optimal Sensor Rules for Binary Decision Given Fusion Rule Formulation for Bayes Binary Decision Formulation of Fusion Rules via Polynomials of Sensor Rules Fixed-Point Type Necessary Condition for the Optimal Sensor Rules Finite Convergence of the Discretized Algorithm Unified Fusion Rule Expression of the Unified Fusion Rule Numerical Examples Two Sensors Three Sensors Four Sensors Extension to Neyman-Pearson Decision Algorithm Searching for Optimal Sensor Rules Numerical Examples General Network Statistical Decision Fusion Elementary Network Structures Parallel Network Tandem Network Hybrid (Tree) NetworkFormulation of Fusion Rule via Polynomials of Sensor Rules Fixed-Point Type Necessary Condition for Optimal Sensor Rules Iterative Algorithm and Convergence Unified Fusion Rule Unified Fusion Rule for Parallel Networks Unified Fusion Rule for Tandem and Hybrid Networks Numerical Examples Three-Sensor System Four-Sensor SystemOptimal Decision Fusion with Given Sensor Rules Problem Formulation Computation of Likelihood Ratios Locally Optimal Sensor Decision Rules with Communications among Sensors Numerical Examples Two-Sensor Neyman-Pearson Decision System Three-Sensor Bayesian Decision System Simultaneous Search for Optimal Sensor Rules and Fusion Rule Problem Formulation Necessary Conditions for Optimal Sensor Rules and an Optimal Fusion Rule Iterative Algorithm and Its Convergence Extensions to Multiple-Bit Compression and Network Decision Systems Extensions to theMultiple-Bit Compression Extensions to Hybrid Parallel Decision System and Tree Network Decision System Numerical Examples Two Examples for Algorithm 3.2 An Example for Algorithm 3.3Performance Analysis of Communication Direction for Two-Sensor Tandem Binary Decision System Problem Formulation SystemModel Bayes Decision Region of Sensor 2 Bayes Decision Region of Sensor 1 (Fusion Center) Bayes Cost Function Results Numerical Examples Network Decision Systems with Channel Errors Some Formulations about Channel Error Necessary Condition for Optimal Sensor Rules Given a Fusion Rule Special Case: Mutually Independent Sensor Observations Unified Fusion Rules for Network Decision Systems Network Decision Structures with Channel Errors Unified Fusion Rule in Parallel Bayesian Binary Decision System Unified Fusion rules for General Network Decision Systems with Channel Errors Numerical Examples Parallel Bayesian Binary Decision System Three-Sensor Decision System Some Uncertain Decision CombinationsRepresentation of Uncertainties Dempster Combination Rule Based on Random Set Formulation Dempster's Combination Rule Mutual Conversion of the Basic Probability Assignment and the Random Set Combination Rules of the Dempster-Shafer Evidences via Random Set Formulation All Possible Random Set Combination Rules Correlated Sensor Basic Probabilistic Assignments Optimal Bayesian Combination Rule Examples of Optimal Combination RuleFuzzy Set Combination Rule Based on Random Set Formulation Mutual Conversion of the Fuzzy Set and the Random Set Some Popular Combination Rules of Fuzzy Sets General Combination Rules Using the Operations of Sets Only Using the More General Correlation of the Random Variables Relationship between the t-Norm and Two-Dimensional Distribution Function Examples Hybrid Combination Rule Based on Random Set FormulationConvex Linear Estimation FusionLMSE Estimation Fusion Formulation of LMSE Fusion Optimal FusionWeights Efficient Iterative Algorithm for Optimal Fusion AppropriateWeightingMatrix Iterative Formula of OptimalWeightingMatrix Iterative Algorithm for Optimal Estimation Fusion ExamplesRecursion of Estimation Error Covariance in Dynamic Systems Optimal Dimensionality Compression for Sensor Data in Estimation Fusion Problem Formulation Preliminary Analytic Solution for Single-Sensor Case Search for Optimal Solution in the Multisensor Case Existence of the Optimal Solution Optimal Solution at a Sensor While Other Sensor Compression Matrices Are Given Numerical Example Quantization of Sensor Data Problem Formulation Necessary Conditions for Optimal Sensor Quantization Rules and Optimal Linear Estimation Fusion Gauss-Seidel Iterative Algorithm for Optimal Sensor Quantization Rules and Linear Estimation Fusion Numerical ExamplesKalman Filtering FusionDistributed Kalman Filtering Fusion with Cross-Correlated Sensor Noises Problem Formulation Distributed Kalman Filtering Fusion without Feedback Optimality of Kalman Filtering Fusion with Feedback Global Optimality of the Feedback Filtering Fusion Local Estimate Errors The Advantages of the Feedback Distributed Kalman Filtering Fusion with Singular Covariances of Filtering Error and Measurement Noises Equivalence Fusion Algorithm LMSE Fusion Algorithm Numerical Examples Optimal Kalman Filtering Trajectory Update with Unideal Sensor Messages Optimal Local-processor Trajectory Update with Unideal Measurements Optimal Local-Processor Trajectory Update with Addition of OOSMs Optimal Local-Processor Trajectory Update with emoval of Earlier Measurement Optimal Local-Processor Trajectory Update with Sequentially Processing Unideal Measurements Numerical Examples Optimal Distributed Fusion Trajectory Update with Local-Processor Unideal Updates Optimal Distributed Fusion Trajectory Update with Addition of Local OOSMUpdate Optimal Distributed State Trajectory Update with Removal of Earlier Local Estimate Optimal Distributed Fusion Trajectory Update with Sequential Processing of Local Unideal Updates Random Parameter Matrices Kalman Filtering Fusion Random Parameter Matrices Kalman Filtering Random Parameter Matrices Kalman Filtering with Multisensor Fusion Some Applications Application to Dynamic Process with False Alarm Application to Multiple-Model Dynamic Process Novel Data Association Method Based on the Integrated Random Parameter Matrices Kalman Filtering Some Traditional Data Association Algorithms Single-Sensor DAIRKF Multisensor DAIRKF Numerical Examples Distributed Kalman Filtering Fusion with Packet Loss/Intermittent Communications Traditional Fusion Algorithms with Packet Loss Sensors Send Raw Measurements to Fusion Center Sensors Send Partial Estimates to Fusion Center Sensors Send Optimal Local Estimates to Fusion Center RemodeledMultisensor System Distributed Kalman Filtering Fusion with Sensor Noises Cross-Correlated and Correlated to Process Noise Optimal Distributed Kalman Filtering Fusion with Intermittent Sensor Transmissions or Packet Loss Suboptimal Distributed Kalman Filtering Fusion with Intermittent Sensor Transmissions or Packet Loss Robust Estimation Fusion Robust LinearMSE Estimation Fusion Minimizing Euclidean Error Estimation Fusion for Uncertain Dynamic System Preliminaries
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Problem Formulation of Centralized Fusion State Bounding Box Estimation Based on Centralized Fusion State Bounding Box Estimation Based on Distributed Fusion Measures of Size of an Ellipsoid or a Box Centralized Fusion Distributed Fusion Fusion of Multiple Algorithms Numerical Examples Figures 7.4 through 7.7 for Comparisons between Algorithms 7.1 and 7.2 Figures 7.8 through 7.10 for Fusion of Multiple AlgorithmsMinimized Euclidean Error Data Association for Uncertain Dynamic System Formulation of Data Association MEEDA Algorithms Numerical Examples References Index
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| Abstract
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Due to the increased capability, reliability, robustness, and survivability of systems with multiple distributed sensors, multi-source information fusion has become a crucial technique in a growing number of areas-including sensor networks, space technology, air traffic control, military engineering, agriculture and environmental engineering, and industrial control. Networked Multisensor Decision and Estimation Fusion: Based on Advanced Mathematical Methods presents advanced mathematical descriptions and methods to help readers achieve more thorough results under more general conditions than w.
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| Subject
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Multisensor data fusion -- Mathematics.
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| Subject
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Multisensor data fusion / Mathematics.
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| Subject
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Sensor networks.
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