| Preface | p. xiii |
| Introduction to Multisensor Data Fusion | p. 1 |
| Introduction | p. 1 |
| Fusion Applications | p. 3 |
| Sensors and Sensor Data | p. 8 |
| The Inference Hierarchy: Output Data | p. 16 |
| A Data Fusion Model | p. 18 |
| Benefits of Data Fusion | p. 22 |
| Architectural Concepts and Issues | p. 27 |
| Limitations of Data Fusion | p. 32 |
| Introduction to the Joint Directors of Laboratories (JDL) Data Fusion Process Model and Taxonomy of Algorithms | p. 37 |
| Introduction to the JDL Data Fusion Processing Model | p. 37 |
| Level 1 Fusion Algorithms | p. 42 |
| Data Alignment | p. 44 |
| Data/Object Correlation | p. 44 |
| Object Position, Kinematic, and Attribute Estimation | p. 45 |
| Object Identity Estimation | p. 47 |
| Level 2 Fusion Algorithms | p. 54 |
| Level 3 Fusion Algorithms | p. 57 |
| Level 4 Fusion Algorithms | p. 59 |
| Level 5 Fusion Techniques | p. 62 |
| Ancillary Support Functions | p. 65 |
| Alternative Data Fusion Process Models | p. 66 |
| Dasarathy's Functional Model | p. 66 |
| Boyd's Decision Loop | p. 67 |
| Bedworth and O'Brien's Omnibus Process Model | p. 68 |
| TRIP Model | p. 69 |
| Level 1 Processing: Data Association and Correlation | p. 73 |
| Introduction | p. 73 |
| Process Model for Correlation | p. 78 |
| Hypothesis Generation | p. 80 |
| Characterizing the Hypothesis Generation Problem | p. 85 |
| Overview of Hypothesis Generation Techniques | p. 92 |
| Hypothesis Evaluation | p. 99 |
| Characterizing the Hypothesis Evaluation Problem | p. 101 |
| Overview of Hypothesis Evaluation Techniques | p. 105 |
| Hypothesis Selection Techniques | p. 109 |
| Defining the Hypothesis Selection Space | p. 112 |
| Overview of Hypothesis Selection Techniques | p. 116 |
| Level 1 Fusion: Kinematic and Attribute Estimation | p. 129 |
| Introduction | p. 129 |
| Overview of Estimation Techniques | p. 132 |
| System Models | p. 133 |
| Optimization Criteria | p. 136 |
| Optimization Approach | p. 140 |
| Processing Approach | p. 143 |
| Batch Estimation | p. 144 |
| Derivation of Weighted Least Squares Solution | p. 144 |
| Processing Flow | p. 149 |
| Batch Processing Implementation Issues | p. 152 |
| Sequential Estimation | p. 153 |
| Derivation of Sequential Weighted Least Squares Solution | p. 154 |
| Sequential Estimation Processing Flow | p. 156 |
| Sequential Processing Implementation Issues | p. 159 |
| The Alpha-Beta Filter | p. 160 |
| Covariance Error Estimation | p. 163 |
| Recent Developments in Estimation | p. 166 |
| Identity Declaration | p. 171 |
| Identity Declaration and Pattern Recognition | p. 171 |
| Feature Extraction | p. 178 |
| Parametric Templates | p. 185 |
| Cluster Analysis Techniques | p. 187 |
| Adaptive Neural Networks | p. 193 |
| Physical Models | p. 196 |
| Knowledge-Based Methods | p. 198 |
| Hybrid Techniques | p. 200 |
| Decision-Level Identity Fusion | p. 205 |
| Introduction | p. 205 |
| Classical Inference | p. 209 |
| Bayesian Inference | p. 214 |
| Dempster-Shafer's Method | p. 220 |
| Generalized Evidence Processing (GEP) Theory | p. 229 |
| Heuristic Methods for Identity Fusion | p. 231 |
| Implementation and Trade-Offs | p. 234 |
| Inference Accuracy and Performance | p. 235 |
| Computer Resource Requirements | p. 236 |
| A Priori Data Requirements | p. 236 |
| Knowledge-Based Approaches | p. 239 |
| Brief Introduction to Artificial Intelligence | p. 239 |
| Overview of Expert Systems | p. 245 |
| Expert System Concept | p. 245 |
| The Inference Process | p. 247 |
| Forward and Backward Chaining | p. 249 |
| Knowledge Representation | p. 250 |
| Representing Uncertainty | p. 253 |
| Search Techniques | p. 260 |
| Architectures for Knowledge-Based Systems | p. 263 |
| Implementation of Expert Systems | p. 266 |
| Life-Cycle Development Model for Expert Systems | p. 266 |
| Knowledge Engineering | p. 269 |
| Test and Evaluation | p. 272 |
| Expert System Development Tools | p. 275 |
| Logical Templating Techniques | p. 278 |
| Bayes Belief Systems | p. 283 |
| Intelligent Agent Systems | p. 285 |
| Level 4 Processing: Process Monitoring and Optimization | p. 291 |
| Introduction | p. 291 |
| Extending the Concept of Level 4 Processing | p. 297 |
| Techniques for Level 4 Processing | p. 300 |
| Sensor Management Functions | p. 300 |
| General Sensor Controls | p. 302 |
| Optimization of System Resources | p. 305 |
| Measures of Effectiveness and Performance | p. 306 |
| Auction-Based Methods | p. 308 |
| Market Components | p. 309 |
| Multiattribute Auctions | p. 310 |
| Multiattribute Auction Algorithms | p. 311 |
| Research Issues in Level 4 Processing | p. 311 |
| Level 5: Cognitive Refinement and Human-Computer Interaction | p. 315 |
| Introduction | p. 315 |
| Cognitive Aspects of Situation Assessment | p. 317 |
| Individual Differences in Information Processing | p. 320 |
| Enabling HCI Technologies | p. 320 |
| Visual and Graphical Interfaces | p. 321 |
| Aural Interfaces and Natural Language Processing (NLP) | p. 325 |
| Haptic Interfaces | p. 327 |
| Gesture Recognition | p. 328 |
| Wearable Computers | p. 329 |
| Computer-Aided Situation Assessment | p. 330 |
| Computer-Aided Cognition | p. 330 |
| Utilization of Language Constructs | p. 331 |
| Areas for Research | p. 334 |
| An SBIR Multimode Experiment in Computer-Based Training | p. 336 |
| SBIR Objective | p. 336 |
| Experimental Design and Test Approach | p. 337 |
| CBT Implementation | p. 338 |
| Summary of Results | p. 340 |
| Implications for Data Fusion Systems | p. 341 |
| Implementing Data Fusion Systems | p. 345 |
| Introduction | p. 345 |
| Requirements Analysis and Definition | p. 349 |
| Sensor Selection and Evaluation | p. 351 |
| Functional Allocation and Decomposition | p. 356 |
| Architecture Trade-Offs | p. 358 |
| Algorithm Selection | p. 364 |
| Database Definition | p. 369 |
| HCI Design | p. 373 |
| Software Implementation | p. 377 |
| Test and Evaluation | p. 379 |
| Emerging Applications of Multisensor Data Fusion | p. 385 |
| Introduction | p. 385 |
| Survey of Military Applications | p. 386 |
| Emerging Nonmilitary Applications | p. 392 |
| Intelligent Monitoring of Complex Systems | p. 393 |
| Medical Applications | p. 396 |
| Law Enforcement | p. 397 |
| Nondestructive Testing (NDT) | p. 398 |
| Robotics | p. 398 |
| Commercial Off The Shelf (COTS) Tools | p. 399 |
| Survey of COTS Software | p. 399 |
| Special Purpose COTS Software | p. 399 |
| General Purpose Data Fusion Software | p. 402 |
| A Survey of Surveys | p. 406 |
| Perspectives and Comments | p. 408 |
| Automated Information Management | p. 415 |
| Introduction | p. 415 |
| Initial Automated Information Manager: Automated Targeting Data Fusion | p. 419 |
| Automated Targeting Data Fusion: Structure and Flow | p. 424 |
| Automatic Information Needs Resolution Example: Automated Imagery Corroboration | p. 433 |
| Automated Image Corroboration Example | p. 436 |
| Automated Information Manager: Ubiquitous Utility | p. 441 |
| About The Authors | p. 445 |
| Index | p. 447 |
| Table of Contents provided by Rittenhouse. All Rights Reserved. |
