
High-Level Data Fusion
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Content
- High-Level Data Fusion
- Table of Contents
- Preface
- Chapter 1: Models, Architectures, and Data
- 1.1 WHAT IS HIGH-LEVEL FUSION?
- 1.2 FUSION MODELS
- 1.2.1 JDL Model
- 1.2.2 DIKW Hierarchy and Abstraction of Knowledge
- 1.2.3 Assessment Versus Awareness
- 1.2.4 OODA Loop
- 1.2.5 Rasmussen Information Processing Hierarchy
- 1.2.6 Correspondence among Models
- 1.3 SENSORS AND INTELLIGENCE
- 1.3.1 Signals Intelligence (SIGINT)
- 1.3.2 Imagery Intelligence (IMINT)
- 1.3.3 Measurement and Signature Intelligence (MASINT)
- 1.3.4 Human Intelligence (HUMINT)
- 1.3.5 Open Source Intelligence (OSINT)
- 1.3.6 Geospatial Intelligence (GEOINT)
- 1.3.7 Intelligent Data Format
- 1.4 GENERIC FUSION ARCHITECTURE AND BOOK SCOPE
- 1.5 FURTHER READING
- Chapter 2: Mathematical Preliminaries
- 2.1 USAGE OF SYMBOLS
- 2.2 GRAPHS AND TREES
- 2.3 PROBABILITY AND STATISTICS
- 2.3.1 Probability Distributions
- 2.4 MATHEMATICAL LOGIC
- 2.5 ALGORITHMIC COMPLEXITY
- 2.6 FURTHER READING
- Chapter 3: Approaches to Handling Uncertainty
- 3.1 IGNORANCE TO UNCERTAINTIES
- 3.2 APPROACHES TO HANDLING UNCERTAINTIES
- 3.3 NEO-PROBABILIST APPROACH
- 3.3.1 Bayesian Belief Networks (BNs)
- 3.4 NEO-CALCULIST APPROACH
- 3.4.1 Theory of Belief Functions
- 3.4.2 Certainty Factors
- 3.5 NEO-LOGICIST APPROACH
- 3.5.1 Default Logic
- 3.5.2 Program Completion
- 3.6 NEO-POSSIBILIST APPROACHES
- 3.6.1 Fuzzy Sets
- 3.6.2 Fuzzy Logic
- 3.6.3 Possibility Theory
- 3.6.4 Possibilistic Logic
- 3.7 TRANSFORMATION BETWEEN FORMALISMS
- 3.7.1 Transferable Belief Model
- 3.7.2 Relating Probability and Possibility
- 3.8 FURTHER READING
- Chapter 4: Introduction to Target Tracking
- 4.1 TARGET TRACKING CONCEPT AND ARCHITECTURE
- 4.2 TARGET TRACKING PROBLEM MODELING
- 4.2.1 State Transition and Observation Models
- 4.2.2 Estimation Problem
- 4.3 SINGLE SENSOR SINGLE TARGET TRACKING
- 4.3.1 Alpha-Beta Filter
- 4.3.2 Kalman Filter (KF)
- 4.4 GATING AND DATA ASSOCIATION
- 4.5 MULTISENSOR SINGLE TARGET TRACKING (IN CLUTTER)
- 4.5.1 Probabilistic Data Association Filter (PDAF)
- 4.6 MULTISENSOR MULTITARGET TRACKING (IN CLUTTER)
- 4.6.1 Joint Probabilistic Data Association (JPDA)
- 4.6.2 Multiple-Hypothesis Tracking (MHT)
- 4.7 INTERACTING MULTIPLE MODEL (IMM)
- 4.8 CRAMER-RAO LOWER BOUND (CRLB)
- 4.9 FURTHER READING
- Chapter 5: Target Classification and Aggregation
- 5.1 TARGET CLASSIFICATION
- 5.1.1 Example Surveillance Scenario
- 5.1.2 Naïve Bayesian Classifier (NBC) for Target Classification
- 5.1.3 Rule-Based Expert Systems for Target Classification
- 5.1.4 Dempster-Shafer Theory for Target Classification
- 5.1.5 Fuzzy Logic for Target Classification
- 5.2 TARGETS AGGREGATION
- 5.2.1 Spatiotemporal Clustering (STC) Concept
- 5.2.2 Manhattan Distance-Based Grid-Constrained Clustering
- 5.2.3 Directivity- and Displacement-Based Unconstrained Clustering
- 5.2.4 Orthogonality-Based Clustering
- 5.2.5 Singular Value Decomposition-Based Clustering
- 5.2.6 Preprocessing through Entropy Measure
- 5.3 FURTHER READING
- Chapter 6: Model-Based Situation Assessment
- 6.1 BAYESIAN BELIEF NETWORKS
- 6.2 CONDITIONAL INDEPENDENCE IN BELIEF NETWORKS
- 6.3 EVIDENCE, BELIEF, AND LIKELIHOOD
- 6.4 PRIOR PROBABILITIES IN NETWORKS WITHOUT EVIDENCE
- 6.5 BELIEF REVISION
- 6.6 EVIDENCE PROPAGATION IN POLYTREES
- 6.6.1 Upward Propagation in a Linear Fragment
- 6.6.2 Downward Propagation in a Linear Fragment
- 6.6.3 Upward Propagation in a Tree Fragment
- 6.6.4 Downward Propagation in a Tree Fragment
- 6.6.5 Upward Propagation in a Polytree Fragment
- 6.6.6 Downward Propagation in a Polytree Fragment
- 6.6.7 Propagation Algorithm
- 6.7 EVIDENCE PROPAGATION IN DIRECTED ACYCLIC GRAPHS
- 6.7.1 Graphical Transformation
- 6.7.2 Join Tree Initialization
- 6.7.3 Propagation in Join Tree and Marginalization
- 6.7.4 Handling Evidence
- 6.8 COMPLEXITY OF INFERENCE ALGORITHMS
- 6.9 ACQUISITION OF PROBABILITIES
- 6.10 ADVANTAGES AND DISADVANTAGES OF BELIEF NETWORKS
- 6.11 THEATER MISSILE DEFENSE APPLICATION
- 6.12 BELIEF NETWORK TOOLS
- 6.13 FURTHER READING
- Chapter 7: Modeling Time for Situation Assessment
- 7.1 MARKOV MODELS
- 7.2 HIDDEN MARKOV MODELS (HMM)
- 7.2.1 The Forward Algorithm
- 7.2.2 The Viterbi Algorithm
- 7.3 HIERARCHICAL HIDDEN MARKOV MODELS (HHMM)
- 7.3.1 The Forward Algorithm for HHMM
- 7.3.2 The Viterbi Algorithm for HHMM
- 7.4 MARKOV MODELS FOR TEXT ANALYSES
- 7.5 HMM WITH EXPLICIT STATE DURATION
- 7.6 DYNAMIC BAYESIAN NETWORKS (DBNs)
- 7.6.1 Inference Algorithms for DBNs
- 7.7 DBN APPLICATION FOR LIFE STATUS ESTIMATION
- 7.8 FURTHER READING
- Chapter 8: Handling Nonlinear and Hybrid Models
- 8.1 EXTENDED KALMAN FILTER (EKF)
- 8.2 UNSCENTED KALMAN FILTER (UKF)
- 8.3 PARTICLE FILTER (PF)
- 8.3.1 Basic Particle Filter
- 8.3.2 Particle Filter Algorithms
- 8.3.3 Rao-Blackwellised Particle Filter (RBPF)
- 8.3.4 Multitarget Tracking and Particle Filters
- 8.3.5 Tracking a Variable Number of Targets via DBNs
- 8.3.6 Particle Filter for DBN
- 8.3.7 Example DBN Inferencing by Particle Filtering
- 8.3.8 Particle Filter Issues
- 8.4 FURTHER READING
- Chapter 9: Decision Support
- 9.1 EXPECTED UTILITY THEORY AND DECISION TREES
- 9.2 INFLUENCE DIAGRAMS FOR DECISION SUPPORT
- 9.2.1 Inferencing in Influence Diagrams
- 9.2.2 Compilation of Influence Diagrams
- 9.2.3 Inferencing in Strong Junction Trees
- 9.2.4 An Example Influence Diagram for Theater Missile Defense
- 9.3 SYMBOLIC ARGUMENTATION FOR DECISION SUPPORT
- 9.3.1 Measuring Consensus
- 9.3.2 Combining Sources of Varying Confidence
- 9.4 FURTHER READING
- Chapter 10: Learning of Fusion Models
- 10.1 LEARNING NAÏVE BAYESIAN CLASSIFIERS
- 10.2 RULE LEARNING FROM DECISION TREE ALGORITHMS
- 10.2.1 Algorithms for Constructing Decision Trees
- 10.2.2 Overfitting in Decision Trees
- 10.2.3 Handling Continuous Attributes
- 10.3 BAYESIAN BELIEF NETWORK LEARNING
- 10.3.1 Learning Probabilities: Brief Survey
- 10.3.2 Learning Probabilities from Fully Observable Variables
- 10.3.3 Learning Probabilities from Partially Observable Variables
- 10.3.4 Online Adjustment of Parameters
- 10.3.5 Brief Survey of Structure Learning
- 10.3.6 Learning Structure from Fully Observable Variables
- 10.3.7 Learning Structure from Partially Observable Variables
- 10.3.8 Use of Prior Knowledge from Experts
- 10.4 BAUM-WELCH ALGORITHM FOR LEARNING HMM
- 10.4.1 Generalized Baum-Welch Algorithm for HHMM
- 10.5 FURTHER READING
- Chapter 11: Towards Cognitive Agents for Data Fusion
- 11.1 MOTIVATION AND SCOPE
- 11.2 ENVELOPE MODEL OF HUMAN COGNITION
- 11.3 COMPARATIVE STUDY
- 11.3.1 Classical Cognitive Architectures and Envelope
- 11.3.2 Agent Architectures and Envelope
- 11.3.3 C4I Architectures and Envelope
- 11.4 LEARNING, SYSTEMATICITY, AND LOGICAL OMNISCIENCE
- 11.5 COMPUTATIONAL REALIZATION
- 11.6 SOME DISCUSSION
- 11.7 FURTHER READING
- Chapter 12: Distributed Fusion
- 12.1 CONCEPT AND APPROACH
- 12.2 DISTRIBUTED FUSION ENVIRONMENTS
- 12.3 ALGORITHM FOR DISTRIBUTED SITUATION ASSESSMENT
- 12.4 DISTRIBUTED KALMAN FILTER
- 12.5 RELEVANCE TO NETWORK CENTRIC WARFARE
- 12.6 FURTHER READING
- References
- About the Author
- Index
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