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| Preface | |
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| Introduction to Multisensor Data Fusion | |
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| Introduction | |
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| Fusion Applications | |
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| Sensors and Sensor Data | |
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| The Inference Hierarchy: Output Data | |
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| A Data Fusion Model | |
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| Benefits of Data Fusion | |
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| Architectural Concepts and Issues | |
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| Limitations of Data Fusion | |
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| Introduction to the Joint Directors of Laboratories (JDL) Data Fusion Process Model and Taxonomy of Algorithms | |
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| Introduction to the JDL Data Fusion Processing Model | |
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| Level 1 Fusion Algorithms | |
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| Data Alignment | |
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| Data/Object Correlation | |
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| Object Position, Kinematic, and Attribute Estimation | |
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| Object Identity Estimation | |
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| Level 2 Fusion Algorithms | |
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| Level 3 Fusion Algorithms | |
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| Level 4 Fusion Algorithms | |
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| Level 5 Fusion Techniques | |
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| Ancillary Support Functions | |
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| Alternative Data Fusion Process Models | |
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| Dasarathy's Functional Model | |
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| Boyd's Decision Loop | |
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| Bedworth and O'Brien's Omnibus Process Model | |
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| TRIP Model | |
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| Level 1 Processing: Data Association and Correlation | |
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| Introduction | |
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| Process Model for Correlation | |
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| Hypothesis Generation | |
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| Characterizing the Hypothesis Generation Problem | |
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| Overview of Hypothesis Generation Techniques | |
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| Hypothesis Evaluation | |
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| Characterizing the Hypothesis Evaluation Problem | |
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| Overview of Hypothesis Evaluation Techniques | |
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| Hypothesis Selection Techniques | |
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| Defining the Hypothesis Selection Space | |
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| Overview of Hypothesis Selection Techniques | |
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| Level 1 Fusion: Kinematic and Attribute Estimation | |
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| Introduction | |
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| Overview of Estimation Techniques | |
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| System Models | |
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| Optimization Criteria | |
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| Optimization Approach | |
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| Processing Approach | |
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| Batch Estimation | |
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| Derivation of Weighted Least Squares Solution | |
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| Processing Flow | |
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| Batch Processing Implementation Issues | |
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| Sequential Estimation | |
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| Derivation of Sequential Weighted Least Squares Solution | |
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| Sequential Estimation Processing Flow | |
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| Sequential Processing Implementation Issues | |
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| The Alpha-Beta Filter | |
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| Covariance Error Estimation | |
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| Recent Developments in Estimation | |
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| Identity Declaration | |
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| Identity Declaration and Pattern Recognition | |
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| Feature Extraction | |
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| Parametric Templates | |
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| Cluster Analysis Techniques | |
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| Adaptive Neural Networks | |
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| Physical Models | |
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| Knowledge-Based Methods | |
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| Hybrid Techniques | |
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| Decision-Level Identity Fusion | |
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| Introduction | |
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| Classical Inference | |
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| Bayesian Inference | |
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| Dempster-Shafer's Method | |
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| Generalized Evidence Processing (GEP) Theory | |
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| Heuristic Methods for Identity Fusion | |
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| Implementation and Trade-Offs | |
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| Inference Accuracy and Performance | |
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| Computer Resource Requirements | |
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| A Priori Data Requirements | |
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| Knowledge-Based Approaches | |
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| Brief Introduction to Artificial Intelligence | |
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| Overview of Expert Systems | |
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| Expert System Concept | |
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| The Inference Process | |
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| Forward and Backward Chaining | |
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| Knowledge Representation | |
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| Representing Uncertainty | |
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| Search Techniques | |
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| Architectures for Knowledge-Based Systems | |
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| Implementation of Expert Systems | |
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| Life-Cycle Development Model for Expert Systems | |
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| Knowledge Engineering | |
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| Test and Evaluation | |
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| Expert System Development Tools | |
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| Logical Templating Techniques | |
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| Bayes Belief Systems | |
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| Intelligent Agent Systems | |
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| Level 4 Processing: Process Monitoring and Optimization | |
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| Introduction | |
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| Extending the Concept of Level 4 Processing | |
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| Techniques for Level 4 Processing | |
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| Sensor Management Functions | |
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| General Sensor Controls | |
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| Optimization of System Resources | |
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| Measures of Effectiveness and Performance | |
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| Auction-Based Methods | |
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| Market Components | |
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| Multiattribute Auctions | |
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| Multiattribute Auction Algorithms | |
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| Research Issues in Level 4 Processing | |
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| Level 5: Cognitive Refinement and Human-Computer Interaction | |
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| Introduction | |
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| Cognitive Aspects of Situation Assessment | |
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| Individual Differences in Information Processing | |
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| Enabling HCI Technologies | |
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| Visual and Graphical Interfaces | |
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| Aural Interfaces and Natural Language Processing (NLP) | |
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| Haptic Interfaces | |
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| Gesture Recognition | |
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| Wearable Computers | |
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| Computer-Aided Situation Assessment | |
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| Computer-Aided Cognition | |
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| Utilization of Language Constructs | |
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| Areas for Research | |
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| An SBIR Multimode Experiment in Computer-Based Training | |
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| SBIR Objective | |
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| Experimental Design and Test Approach | |
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| CBT Implementation | |
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| Summary of Results | |
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| Implications for Data Fusion Systems | |
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| Implementing Data Fusion Systems | |
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| Introduction | |
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| Requirements Analysis and Definition | |
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| Sensor Selection and Evaluation | |
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| Functional Allocation and Decomposition | |
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| Architecture Trade-Offs | |
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| Algorithm Selection | |
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| Database Definition | |
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| HCI Design | |
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| Software Implementation | |
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| Test and Evaluation | |
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| Emerging Applications of Multisensor Data Fusion | |
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| Introduction | |
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| Survey of Military Applications | |
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| Emerging Nonmilitary Applications | |
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| Intelligent Monitoring of Complex Systems | |
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| Medical Applications | |
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| Law Enforcement | |
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| Nondestructive Testing (NDT) | |
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| Robotics | |
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| Commercial Off The Shelf (COTS) Tools | |
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| Survey of COTS Software | |
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| Special Purpose COTS Software | |
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| General Purpose Data Fusion Software | |
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| A Survey of Surveys | |
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| Perspectives and Comments | |
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| Automated Information Management | |
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| Introduction | |
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| Initial Automated Information Manager: Automated Targeting Data Fusion | |
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| Automated Targeting Data Fusion: Structure and Flow | |
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| Automatic Information Needs Resolution Example: Automated Imagery Corroboration | |
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| Automated Image Corroboration Example | |
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| Automated Information Manager: Ubiquitous Utility | |
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| About The Authors | |
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| Index | |