Mathematical techniques in multi-sensor data fusion / David L. Hall.

Format:

Book

Author/Creator:

Hall, David L. (David Lee), 1946-2015.

Contributor:

Rosengarten Family Fund.

Language:

English

Subjects (All):

Signal processing.

Multisensor data fusion--Mathematical models.

Multisensor data fusion.

Physical Description:

xiii, 449 pages : illustrations ; 24 cm

Edition:

Second edition.

Other Title:

Mathematical techniques in multisensor data fusion

Place of Publication:

Boston : Artech House, [2004]

Contents:

Chapter 1 Introduction to Multisensor Data Fusion 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

3.2 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

4.2 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

6.2 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

8.2 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

9.2 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

10.2 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

10.8 HCI Design 373

10.9 Software Implementation 377

10.10 Test and Evaluation 379

Chapter 11 Emerging Applications of Multisensor Data Fusion 385

11.2 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

Chapter 12 Automated Information Management 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.

Notes:

Includes bibliographical references and index.

Local Notes:

Acquired for the Penn Libraries with assistance from the Rosengarten Family Fund.

ISBN:

1580533353

OCLC:

54783068