| Preface | xiii |
Chapter 1 | Introduction to Multisensor Data Fusion | 1 |
1.1 | Introduction | 1 |
1.2 | Fusion Applications | 3 |
1.3 | Sensors and Sensor Data | 8 |
1.4 | The Inference Hierarchy: Output Data | 16 |
1.5 | A Data Fusion Model | 18 |
1.6 | Benefits of Data Fusion | 22 |
1.7 | Architectural Concepts and Issues | 27 |
1.8 | Limitations of Data Fusion | 32 |
Chapter 2 | Introduction to the Joint Directors of Laboratories (JDL) Data Fusion Process Model and Taxonomy of Algorithms | 37 |
2.1 | Introduction to the JDL Data Fusion Processing Model | 37 |
2.2 | Level 1 Fusion Algorithms | 42 |
2.2.1 | Data Alignment | 44 |
2.2.2 | Data/Object Correlation | 44 |
2.2.3 | Object Position, Kinematic, and Attribute Estimation | 45 |
2.2.4 | Object Identity Estimation | 47 |
2.3 | Level 2 Fusion Algorithms | 54 |
2.4 | Level 3 Fusion Algorithms | 57 |
2.5 | Level 4 Fusion Algorithms | 59 |
2.6 | Level 5 Fusion Techniques | 62 |
2.7 | Ancillary Support Functions | 65 |
2.8 | Alternative Data Fusion Process Models | 66 |
2.8.1 | Dasarathy's Functional Model | 66 |
2.8.2 | Boyd's Decision Loop | 67 |
2.8.3 | Bedworth and O'Brien's Omnibus Process Model | 68 |
2.8.4 | TRIP Model | 69 |
Chapter 3 | Level 1 Processing: Data Association and Correlation | 73 |
3.1 | Introduction | 73 |
3.2 | Process Model for Correlation | 78 |
3.3 | Hypothesis Generation | 80 |
3.3.1 | Characterizing the Hypothesis Generation Problem | 85 |
3.3.2 | Overview of Hypothesis Generation Techniques | 92 |
3.4 | Hypothesis Evaluation | 99 |
3.4.1 | Characterizing the Hypothesis Evaluation Problem | 101 |
3.4.2 | Overview of Hypothesis Evaluation Techniques | 105 |
3.5 | Hypothesis Selection Techniques | 109 |
3.5.1 | Defining the Hypothesis Selection Space | 112 |
3.5.2 | Overview of Hypothesis Selection Techniques | 116 |
Chapter 4 | Level 1 Fusion: Kinematic and Attribute Estimation | 129 |
4.1 | Introduction | 129 |
4.2 | Overview of Estimation Techniques | 132 |
4.2.1 | System Models | 133 |
4.2.2 | Optimization Criteria | 136 |
4.2.3 | Optimization Approach | 140 |
4.2.4 | Processing Approach | 143 |
4.3 | Batch Estimation | 144 |
4.3.1 | Derivation of Weighted Least Squares Solution | 144 |
4.3.2 | Processing Flow | 149 |
4.3.3 | Batch Processing Implementation Issues | 152 |
4.4 | Sequential Estimation | 153 |
4.4.1 | Derivation of Sequential Weighted Least Squares Solution | 154 |
4.4.2 | Sequential Estimation Processing Flow | 156 |
4.4.3 | Sequential Processing Implementation Issues | 159 |
4.4.4 | The Alpha-Beta Filter | 160 |
4.5 | Covariance Error Estimation | 163 |
4.6 | Recent Developments in Estimation | 166 |
Chapter 5 | Identity Declaration | 171 |
5.1 | Identity Declaration and Pattern Recognition | 171 |
5.2 | Feature Extraction | 178 |
5.3 | Parametric Templates | 185 |
5.4 | Cluster Analysis Techniques | 187 |
5.5 | Adaptive Neural Networks | 193 |
5.6 | Physical Models | 196 |
5.7 | Knowledge-Based Methods | 198 |
5.8 | Hybrid Techniques | 200 |
Chapter 6 | Decision-Level Identity Fusion | 205 |
6.1 | Introduction | 205 |
6.2 | Classical Inference | 209 |
6.3 | Bayesian Inference | 214 |
6.4 | Dempster-Shafer's Method | 220 |
6.5 | Generalized Evidence Processing (GEP) Theory | 229 |
6.6 | Heuristic Methods for Identity Fusion | 231 |
6.7 | Implementation and Trade-Offs | 234 |
6.7.1 | Inference Accuracy and Performance | 235 |
6.7.2 | Computer Resource Requirements | 236 |
6.7.3 | A Priori Data Requirements | 236 |
Chapter 7 | Knowledge-Based Approaches | 239 |
7.1 | Brief Introduction to Artificial Intelligence | 239 |
7.2 | Overview of Expert Systems | 245 |
7.2.1 | Expert System Concept | 245 |
7.2.2 | The Inference Process | 247 |
7.2.3 | Forward and Backward Chaining | 249 |
7.2.4 | Knowledge Representation | 250 |
7.2.5 | Representing Uncertainty | 253 |
7.2.6 | Search Techniques | 260 |
7.2.7 | Architectures for Knowledge-Based Systems | 263 |
7.3 | Implementation of Expert Systems | 266 |
7.3.1 | Life-Cycle Development Model for Expert Systems | 266 |
7.3.2 | Knowledge Engineering | 269 |
7.3.3 | Test and Evaluation | 272 |
7.3.4 | Expert System Development Tools | 275 |
7.4 | Logical Templating Techniques | 278 |
7.5 | Bayes Belief Systems | 283 |
7.6 | Intelligent Agent Systems | 285 |
Chapter 8 | Level 4 Processing: Process Monitoring and Optimization | 291 |
8.1 | Introduction | 291 |
8.2 | Extending the Concept of Level 4 Processing | 297 |
8.3 | Techniques for Level 4 Processing | 300 |
8.3.1 | Sensor Management Functions | 300 |
8.3.2 | General Sensor Controls | 302 |
8.3.3 | Optimization of System Resources | 305 |
8.3.4 | Measures of Effectiveness and Performance | 306 |
8.4 | Auction-Based Methods | 308 |
8.4.1 | Market Components | 309 |
8.4.2 | Multiattribute Auctions | 310 |
8.4.3 | Multiattribute Auction Algorithms | 311 |
8.5 | Research Issues in Level 4 Processing | 311 |
Chapter 9 | Level 5: Cognitive Refinement and Human-Computer Interaction | 315 |
9.1 | Introduction | 315 |
9.2 | Cognitive Aspects of Situation Assessment | 317 |
9.3 | Individual Differences in Information Processing | 320 |
9.4 | Enabling HCI Technologies | 320 |
9.4.1 | Visual and Graphical Interfaces | 321 |
9.4.2 | Aural Interfaces and Natural Language Processing (NLP) | 325 |
9.4.3 | Haptic Interfaces | 327 |
9.4.4 | Gesture Recognition | 328 |
9.4.5 | Wearable Computers | 329 |
9.5 | Computer-Aided Situation Assessment | 330 |
9.5.1 | Computer-Aided Cognition | 330 |
9.5.2 | Utilization of Language Constructs | 331 |
9.5.3 | Areas for Research | 334 |
9.6 | An SBIR Multimode Experiment in Computer-Based Training | 336 |
9.6.1 | SBIR Objective | 336 |
9.6.2 | Experimental Design and Test Approach | 337 |
9.6.3 | CBT Implementation | 338 |
9.6.4 | Summary of Results | 340 |
9.6.5 | Implications for Data Fusion Systems | 341 |
Chapter 10 | Implementing Data Fusion Systems | 345 |
10.1 | Introduction | 345 |
10.2 | Requirements Analysis and Definition | 349 |
10.3 | Sensor Selection and Evaluation | 351 |
10.4 | Functional Allocation and Decomposition | 356 |
10.5 | Architecture Trade-Offs | 358 |
10.6 | Algorithm Selection | 364 |
10.7 | Database Definition | 369 |
10.8 | HCI Design | 373 |
10.9 | Software Implementation | 377 |
10.10 | Test and Evaluation | 379 |
Chapter 11 | Emerging Applications of Multisensor Data Fusion | 385 |
11.1 | Introduction | 385 |
11.2 | Survey of Military Applications | 386 |
11.3 | Emerging Nonmilitary Applications | 392 |
11.3.1 | Intelligent Monitoring of Complex Systems | 393 |
11.3.2 | Medical Applications | 396 |
11.3.3 | Law Enforcement | 397 |
11.3.4 | Nondestructive Testing (NDT) | 398 |
11.3.5 | Robotics | 398 |
11.4 | Commercial Off The Shelf (COTS) Tools | 399 |
11.4.1 | Survey of COTS Software | 399 |
11.4.2 | Special Purpose COTS Software | 399 |
11.4.3 | General Purpose Data Fusion Software | 402 |
11.4.4 | A Survey of Surveys | 406 |
11.5 | Perspectives and Comments | 408 |
Chapter 12 | Automated Information Management | 415 |
12.1 | Introduction | 415 |
12.2 | Initial Automated Information Manager: Automated Targeting Data Fusion | 419 |
12.3 | Automated Targeting Data Fusion: Structure and Flow | 424 |
12.4 | Automatic Information Needs Resolution Example: Automated Imagery Corroboration | 433 |
12.4.1 | Automated Image Corroboration Example | 436 |
12.5 | Automated Information Manager: Ubiquitous Utility | 441 |
| About The Authors | 445 |
| Index | 447 |