| Foreword | p. xi |
| Preface | p. xv |
| Introduction | p. 1 |
| Motivations and Applications for Data Fusion | p. 2 |
| A Functional Model of the Data Fusion Process | p. 5 |
| Methods for Implementing Data Fusion | p. 8 |
| The Current State of Data Fusion | p. 11 |
| References | p. 13 |
| Taxonomy of Functional Architectures | p. 15 |
| Generalized Processing Models for Data Fusion | p. 15 |
| Generic Level 1 Processing Architectures | p. 20 |
| Real-World Architectures | p. 23 |
| Functional Requirements for Situation and Threat Assessment | p. 28 |
| Blackboard Architectures and Opportunistic Reasoning | p. 33 |
| Problem-Solving Techniques for Situation and Threat Assessment | p. 33 |
| Concepts of Blackboard Processing | p. 35 |
| Pilot Aiding Applications | p. 38 |
| Some Advantages and Disadvantages of Blackboard Architecture | p. 39 |
| Achieving Real-Time Performance: Parallel Architectures | p. 40 |
| Parallelism in Rule-Based Systems | p. 41 |
| Parallelism in Semantic Networks | p. 43 |
| Parallelism in Logic Programming | p. 44 |
| Comments | p. 44 |
| References | p. 46 |
| Defense Applications of Data Fusion | p. 49 |
| Representative Military Applications | p. 50 |
| Antisubmarine Warfare | p. 51 |
| Tactical Air Warfare | p. 54 |
| Land Battle Battlefield Warfare | p. 57 |
| Use of Sensed Information | p. 58 |
| Military Data Fusion-Decision Support Architecture | p. 60 |
| Data Fusion Subsystem | p. 61 |
| Decision Support Subsystem | p. 66 |
| Human Command and Control | p. 67 |
| Characteristics of Fused Information | p. 67 |
| Special Considerations for Military Systems | p. 73 |
| Information Security | p. 73 |
| Multisensor Countermeasures | p. 74 |
| Data Confidence Identification for Lethal Use | p. 75 |
| Fusion System Classification | p. 75 |
| Comments | p. 75 |
| References | p. 76 |
| Sensors, Sources, and Communication Links | p. 77 |
| Detection and Estimation Functions of Sensors | p. 78 |
| A General Sensor Model | p. 78 |
| Classical Detection and Estimation | p. 80 |
| Sensor and System Performance Measures | p. 84 |
| Detection and Estimation in Data Fusion Systems | p. 86 |
| Alternative Sensor Implementations | p. 88 |
| Comparison of Hard- and Soft-Decision Sensors | p. 92 |
| Numerical Results for a Bayesian Example | p. 93 |
| Criteria for Beneficial Combination | p. 95 |
| Operational Benefits of Soft-Decision Sensors and Fusion | p. 95 |
| Soft-Decision Sensor Processing | p. 97 |
| Target Signatures | p. 97 |
| Features for Imaging and Nonimaging Applications | p. 99 |
| Classification Database Requirements | p. 100 |
| ATR Processes | p. 100 |
| Military Sensors | p. 104 |
| Radar Surveillance and Fire Control Sensors | p. 106 |
| Infrared Search-Track and Imaging Sensors | p. 110 |
| Electro-Optical Sensors | p. 113 |
| Identification Friend or Foe Sensors | p. 113 |
| Electronic Support Measures Sensors | p. 114 |
| Acoustic Sensors | p. 118 |
| Military Source and Data Link Characteristics | p. 120 |
| Source Data | p. 121 |
| Communication Links for Sensor and Source Data | p. 122 |
| A Simple Fusion Network Example | p. 124 |
| References | p. 126 |
| Sensor Management | p. 129 |
| Sensor Management Functions | p. 130 |
| Sensor Interfaces | p. 135 |
| Establishing Target Priority | p. 136 |
| Establishing Priority Autonomously | p. 138 |
| Establishing Priority Cooperatively | p. 141 |
| Sensor-to-Target Assignment Methods | p. 143 |
| Sensor Cueing and Hand-off | p. 147 |
| Sensor Management Applications | p. 151 |
| An Air-Combat Sensor Management Example | p. 151 |
| A Distributed Sensor Network Example | p. 154 |
| References | p. 157 |
| Data Fusion for State Estimation | p. 159 |
| Association of Data and Tracking of Dynamic Targets | p. 160 |
| Static Data Association for Target Localization | p. 160 |
| Dynamic Data Association and Target Tracking | p. 163 |
| The Roles of Association and Estimation | p. 165 |
| Issues in Association and Tracking | p. 167 |
| Static Data Association and Target Position Location | p. 168 |
| Multiple Sensor, Common Dimensionality | p. 168 |
| Multiple Sensor, Different Dimensionality | p. 170 |
| Multiple-Site, Multiple-Sensor | p. 172 |
| Taxonomy of Dynamic Data Association and Tracking Algorithms | p. 175 |
| A General Association and Tracking Loop | p. 175 |
| Taxonomy of Design Approaches | p. 180 |
| Key Design Parameters | p. 185 |
| Report-Track Data Association for Target Tracking | p. 187 |
| Track-Track Data Association | p. 193 |
| Recursive Track-Track Association | p. 193 |
| Batch Track-Track Association | p. 197 |
| State Estimators for Association and Tracking | p. 198 |
| State Estimation | p. 198 |
| Least Squares Estimator | p. 200 |
| Weighted Least Squares | p. 201 |
| Maximum Likelihood Estimator | p. 201 |
| Maximum Likelihood Estimation for Static Target Localization | p. 201 |
| Minimum Variance Estimators for Recursive Tracking | p. 203 |
| The Kalman Filter | p. 204 |
| Tracking Filters for Maneuvering Targets | p. 205 |
| Tracking Filter Implementation Issues | p. 207 |
| Multiple Sensor Estimation Considerations | p. 208 |
| References | p. 210 |
| Data Fusion for Object Identification | p. 213 |
| Overview of Data Fusion Algorithms for Identity Estimation | p. 214 |
| Introduction | p. 214 |
| Taxonomy of Identity Fusion Algorithms | p. 214 |
| Algorithm Descriptions | p. 216 |
| Comparisons Between Bayesian and Dempster-Shafer Techniques | p. 237 |
| Perspectives on the Formalisms | p. 238 |
| Bayesian Probability Theory | p. 239 |
| Dempster-Shafer Evidence Theory | p. 245 |
| Representative Comparison between Bayes and Dempster-Shafer | p. 252 |
| References | p. 260 |
| Military Concepts of Situation and Threat Assessment | p. 263 |
| The Military Problem-Solving Process | p. 265 |
| The SHOR Model of Military Decision Processing | p. 269 |
| Data Processing Tasks in the SHOR Model | p. 272 |
| Information Processing Tasks in the SHOR Model | p. 274 |
| Defining Situation Assessment | p. 277 |
| Dealing with Concealment, Cover, and Deception | p. 278 |
| Summary Comments on Situation Assessment | p. 283 |
| The Character and Composition of the Threat | p. 284 |
| Elements of the Threat | p. 286 |
| Threat Assessment Functions | p. 288 |
| Multiple Perspectives of Threat Elements | p. 288 |
| Summary Comments on Threat Assessment | p. 289 |
| References | p. 290 |
| Implementation Approaches for Situation and Threat Assessment | p. 293 |
| Issues and Problems of Methodological Selection | p. 295 |
| Expectation Template-Based Techniques | p. 300 |
| Intelligence Preparation of the Battlefield | p. 301 |
| Event-Activity Profiling | p. 308 |
| Figure-of-Merit Techniques | p. 316 |
| Knowledge-Based and Expert-System Techniques | p. 327 |
| Cooperative and Adaptive Approaches | p. 337 |
| Allies, Stars/Prm, and Taes: Cooperating and Adaptive Approaches | p. 340 |
| The Role of Performance Models in STA | p. 343 |
| Comments | p. 345 |
| References | p. 346 |
| Data Fusion System Architecture Design | p. 349 |
| The Data Fusion Systems Engineering Process | p. 350 |
| Definition of Mission Requirements | p. 350 |
| Definition of Functional Requirements | p. 353 |
| Sensor Requirements Analysis | p. 353 |
| Subsystem Design Process | p. 354 |
| Design Synthesis | p. 358 |
| Database Management for Data Fusion | p. 358 |
| Level 1: Association and Attribute Refinement | p. 358 |
| Levels 2 and 3: Situation Assessment and Threat Refinement | p. 359 |
| DBMS Considerations for Data Fusion Systems | p. 360 |
| Data Processing for Data Fusion | p. 360 |
| Processing System Architecture | p. 361 |
| Methods of Applying Parallelism to Data Fusion | p. 363 |
| Parallel Computing Architectures Applied to Data Fusion | p. 368 |
| Connectionist Architectures Applied to Data Fusion | p. 370 |
| Centralized System Architectures | p. 373 |
| Integrated System Architectures | p. 376 |
| Integrated Sensors | p. 377 |
| Integrated Avionics Architecture | p. 378 |
| Database and Processing Parametric Requirements | p. 382 |
| References | p. 386 |
| System Modeling and Performance Evaluation | p. 389 |
| The Basic Theory of C[superscript 3] Systems | p. 390 |
| Lanchester Models of Combat | p. 390 |
| Command, Control, and Communication Models | p. 393 |
| Formal Models of the Data Fusion Process | p. 397 |
| Analysis of Data Fusion System Performance | p. 399 |
| Definition of Objective | p. 399 |
| Construct Alternatives | p. 400 |
| Establish Evaluation Criteria | p. 400 |
| Develop Modeling Approach | p. 400 |
| Analysis and Results | p. 402 |
| Relating Fusion Performance to Military Effectiveness | p. 403 |
| Data Fusion System Modeling Considerations | p. 406 |
| Scenario Definition | p. 409 |
| Model Fidelity | p. 409 |
| Sensor Modeling | p. 409 |
| Fusion Process Modeling | p. 413 |
| Simulation Architecture | p. 413 |
| Hierarchical Models | p. 415 |
| Testbeds and Simulations | p. 416 |
| Evaluating Military Worth | p. 419 |
| References | p. 423 |
| The Emerging Role of Artificial Intelligence Techniques | p. 425 |
| Broad Benefits of AI Technology | p. 427 |
| Representative Prescriptive Solutions | p. 433 |
| Applying Planning Theory | p. 434 |
| Applying Knowledge-Based Approaches | p. 437 |
| Technical Issues and Design Factors in Using KBS for Data Fusion | p. 440 |
| Difficulties in the Application of AI Components to Data Fusion | p. 441 |
| Real-Time Processing Requirements and Temporal Variance | p. 443 |
| Combined Symbolic and Numeric Processing | p. 445 |
| Large Data-Knowledge Requirements | p. 446 |
| Uncertainty in Data and Knowledge | p. 447 |
| Human Factors | p. 451 |
| Special Aspects of Testing and Evaluating KBS | p. 452 |
| The Range of Applications of AI to Fusion Problems | p. 454 |
| References | p. 456 |
| Index | p. 461 |
| The Authors | p. 465 |
| Table of Contents provided by Syndetics. All Rights Reserved. |
