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Cover image for High-level data fusion
Title:
High-level data fusion
Personal Author:
Publication Information:
New York : Artech House, 2008
Physical Description:
xix, 373 p. : ill. ; 24 cm.
ISBN:
9781596932814

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30000010196807 QA76.76.E95 D37 2008 Open Access Book Book
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Summary

Summary

Master cutting-edge Level 2 fusion techniques that help you develop powerful situation assessment services with eye-popping capabilities and performance with this trail-blazing resource. The book explores object and situation fusion processes with an appropriate handling of uncertainties, and applies cutting-edge artificial intelligence and emerging technologies like particle filtering, spatiotemporal clustering, net-centricity, agent formalism, and distributed fusion together with essential Level 1 techniques and Level 1/2 interactions. Moreover, it includes all the tools you need to design high-level fusion services, select algorithms and software, simulate performance, and evaluate systems with never-before effectiveness. The book explains the Bayesian, fuzzy, and belief function formalisms of data fusion and a review of Level 1 techniques, including essential target tracking methods. Further, it covers Level 2 fusion methods for applications such as target classification and identification, unit aggregation and ambush detection, threat assessment, and relationships among entities and events, and assessing their suitability and capabilities in each case. The book's detailed discussion of Level 1/2 interactions emphasizes particle filtering techniques as unifying methods for both filtering under Level 1 fusion and inferencing in models for Level 2 fusion. The book also describes various temporal modeling techniques including dynamic Bayesian networks and hidden Markov models, distributed fusion for emerging network centric warfare environments, and the adaptation of fusion processes via machine learning techniques. Packed with real-world examples at every step, this peerless volume serves as an invaluable reference for your research and development of next-generation data fusion tools and services.


Table of Contents

Background and Concepts
JDL Architecture and Situation Assessment
Increasing Level of Abstraction of Knowledge
Assessment vs. Awareness
Example Application Domains
Sensors and Data Sources
OODA and Other Architectures
Agent-Based Situation Assessment
Approaches to Handling Uncertainty-Classification of Uncertainties
Probability Theory: Bayesian Probability
Mathematical Logics: Modal Logics
Neo-Probabilists: Bayesian Belief Networks
Neo-Logicist: Fuzzy Logic
Neo-Calculist: Dempster-Shafer, Certainty Factor
Handling of Noisy and Unstructured Text Data Target Tracking-Single-Sensor Single-Target Tracking
Multi-Sensor Single-Target Tracking (in Clutter)
Multi-Sensor Multi-Target Tracking (in Clutter)
Interacting Multiple Models
Target Classification and Identification-Rule-Based Approaches
Expert Systems: Bayesian and Certainty Factor Formalisms
Symbolic Argumentation: D-S Theory of Belief Function
Learning Rules for Classification
Example Applications: AOC Time-Sensitive-Targets
Unit Aggregation-Spatiotemporal Clustering Concept
Directivity and Displacement-Based Unconstrained Clustering
Singular Value Decomposition-Based Clustering
Preprocessing Through Entropy Measure
Example Applications: Ambush Detection
Model-Based Situation Abstraction-Graphical Models for Situation Assessment
Bayesian Belief Network Technology
Algorithms for Inferencing in Belief Network Models
Handling of Continuous Variables
Adaptation of Belief Network Models
Example Applications
Modeling of Time for Situation Assessment-State Space Models
Hidden Markov Models
Dynamic Bayesian Networks
Particle Filtering (Monte Carlo and Rao-Blackwellized)
Example Applications
Performance Enhancement and Evaluation- Level 2 Feedback for Enhanced Level 1 Fusion
Subjective Evaluation
Quantitative Assessment via ROC. Cramer-Rao Lower Bound
Example Applications
Decision Support-Actions and Utility
Rule-based Decision Support
Expected Utility Theory and Decision Tree
Influence Diagrams
Example Applications Distributed Situation Assessment-Network Centric Warfare
Distribution of Models
Formalism Incompatibilities
Handling of Information Pedigree
Example Applications
Learning of Fusion Models-Rule Learning
Belief Network Learning
Sequence Learning
Hidden Markov Models
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