Networked multisensor decision and estimation fusion : based on advanced mathematical methods /: based on advanced mathematical methods. (2012)

• Record Type: Book
• Further Information: Note: Yunmin Zhu [and four others].
• Other Names: Zhu, Yunmin, 1944-
• Contents: Introduction; Fundamental 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) Network; Formulation 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 System; Optimal 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 3.3; Performance 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 Combinations; Representation 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 Rule; Fuzzy 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 Formulation Convex Linear Estimation Fusion; </S
• Publisher Details: Place of publication not identified : CRC Press
• Publication Date: 2012
• Extent: 1 online resource, illustrations
• Subjects: 681.2; Sensor networks; Multisensor data fusion -- Mathematics; MATHEMATICS / Applied; TECHNOLOGY & ENGINEERING / Electrical; TECHNOLOGY & ENGINEERING / Sensors
• Languages: English
• ISBNs: 9781466576001; 1466576006
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.