Multisensor Data Fusion

By: Edward L. Waltz, James Llinas, Franklin E., Jr. White (Foreword by)

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Hardcover | 19 August 1990

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488 Pages

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24.13 x 16.51 x 3.18

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Explains numeric and symbolic approaches to data association, tracking combination, classification, and situation assessment, and provides an overview of data fusion theory and mathematical formalisms.

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You Can Find This Book In

Non-Fiction Engineering & Technology Electronics & Communications Engineering Electronics Engineering Automatic Control Engineering Communications Engineering & Telecommunications Radar Technology Other Technologies & Applied Sciences Military Engineering Earth Sciences, Geography, Environment, Planning Geography Geographical Information Systems GIS & Remote Sensing

Mathematics Applied Mathematics Energy Technology & Engineering Electrical Engineering

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.

Multisensor Data Fusion

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Hardcover

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