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Sensor and Data Fusion: A Tool for Information Assessment and Decision Making, Second EditionFormat | Member Price | Non-Member Price |
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Book Description

The information in the second edition of this volume has been substantially expanded and updated to incorporate recent approaches to sensor and data fusion, as well as additional application examples. A new chapter about data fusion issues associated with multiple-radar tracking systems has also been added. This chapter includes topics such as sensor registration requirements, Kalman filtering, and a discussion of interacting multiple models. As in the first edition, the book discusses the benefits of sensor fusion that accrue when sensors that operate with different phenomenologies or surveil separate volumes of space are used to gather signatures and data about objects or events in their field of view. Subject matter includes: (1) applications of multiple-sensor systems to vehicular traffic management, target classification and tracking, military and homeland defense, and battlefield assessment; (2) target, background, and atmospheric signature-generation phenomena and modeling; (3) the JDL data fusion model; (4) sensor fusion architectures; and (5) detailed descriptions of algorithms that combine multiple-sensor data from target identity and tracking data fusion architectures. Bayesian, Dempster-Shafer, artificial neural networks, fuzzy logic, voting logic, and passive data association techniques for unambiguous location of targets are among the data fusion techniques that are explored.

Book Details

Table of Contents

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- List of Figures
- List of Tables
- Preface
**Chapter 1. Introduction****Chapter 2. Multiple-Sensor System Applications, Benefits, and Design Considerations**- 2.1 Data Fusion Applications to Multiple-Sensor Systems
- 2.2 Selection of Sensors
- 2.3 Benefits of Multiple-Sensor Systems
- 2.4 Influence of Wavelength on Atmospheric Attenuation
- 2.5 Fog Characterization
- 2.6 Effects of Operating Frequency on MMW Sensor Performance
- 2.7 Absorption of MMW Energy in Rain and Fog
- 2.8 Backscatter of MMW Energy from Rain
- 2.9 Effects of Operating Wavelength on IR Sensor Performance
- 2.10 Visibility Metrics
- 2.10.1 Visibility
- 2.10.2 Meteorological range
- 2.11 Attenuation of IR Energy by Rain
- 2.12 Extinction Coefficient Values (typical)
- 2.13 Summary of Attributes of Electromagnetic Sensors
- 2.14 Atmospheric and Sensor System Computer Simulation Models
- 2.14.1 LOWTRAN attenuation model
- 2.14.2 FASCODE and MODTRAN attenuation models
- 2.14.3 EOSAEL sensor performance modelv
- 2.15 Summary
- References
**Chapter 3. Sensor and Data Fusion Architectures and Algorithms**- 3.1 Definition of Data Fusion
- 3.2 Level 1 Processing
- 3.2.1 Detection, classification, and identification algorithms for data fusion
- 3.2.1.1 Physical models
- 3.2.1.2 Feature-based inference techniques
- 3.2.1.3 Cognitive-based models
- 3.2.2 State estimation and tracking algorithms for data fusion
- 3.2.2.1 Search direction
- 3.2.2.2 Correlation and association of data and tracks
- 3.3 Level 2, 3, and 4 Processing
- 3.3.1 Situation refinement
- 3.3.2 Impact (threat) refinement
- 3.3.2.1 Database management
- 3.3.2.2 Interrelation of data fusion levels in an operational setting
- 3.3.3 Fusion process refinement
- 3.4 Level 5 Fusion: Human-Computer Interface
- 3.5 Duality of Data Fusion and Resource Management
- 3.6 Data Fusion Processor Functions
- 3.7 Definition of an Architecture
- 3.8 Data Fusion Architectures
- 3.8.1 Sensor-level fusion
- 3.8.2 Central-level fusion
- 3.8.3 Hybrid fusion
- 3.8.4 Pixel-level fusion
- 3.8.5 Feature-level fusion
- 3.8.6 Decision-level fusion
- 3.9 Sensor Footprint Registration and Size Considerations
- 3.10 Summary
- References
**Chapter 4. Classical Inference**- 4.1 Estimating the Statistics of a Population
- 4.2 Interpreting the Confidence Interval
- 4.3 Confidence Interval for a Population Mean
- 4.4 Significance Tests for Hypotheses
- 4.5 The z-test for the Population Mean
- 4.6 Tests with Fixed Significance Level
- 4.7 The t-test for a Population Mean
- 4.8 Caution in Use of Significance Tests
- 4.9 Inference as a Decision
- 4.10 Summary
- References
**Chapter 5. Bayesian Inference**- 5.1 Bayes' Rule
- 5.2 Bayes' Rule in Terms of Odds Probability and Likelihood Ratio
- 5.3 Direct Application of Bayes' Rule to Cancer Screening Test Example
- 5.4 The Monty Hall Problem (Let's Make a Deal!)
- 5.4.1 Case-by-case analysis
- 5.4.2 Bayes solution
- 5.5 Comparison of Bayesian Inference with Classical Inference
- 5.6 Application of Bayesian Inference to Fusing Information from Multiple Sources
- 5.7 Combining Multiple-Sensor Information Using the Odds Probability Form of Bayes' Rule
- 5.8 Recursive Bayesian Updating
- 5.9 Posterior Calculation Using Multivalued Hypotheses and Recursive Updating
- 5.10 Enhancing Underground Mine Detection Using Two Sensors Whose Data Are Uncorrelated
- 5.11 Bayesian Inference Applied to Freeway Incident detection
- 5.11.1 Problem development
- 5.11.2 Numerical example
- 5.12 Fusion of Images and Video Sequence Data with Particle Filters
- 5.12.1 Particle filter
- 5.12.2 Application to multiple-sensor, multiple-target imagery
- 5.13 Summary
- References
**Chapter 6. Dempster-Shafer Evidential Theory**- 6.1 Overview of the Process
- 6.2 Implementation of the Method
- 6.3 Support, Plausibility, and Uncertainty Interval
- 6.4 Dempster's Rule for Combination of Multiple-Sensor Data
- 6.4.1 Dempster's rule with empty set elements
- 6.4.2 Dempster's rule with singleton propositions
- 6.5 Comparison of Dempster-Shafer with Bayesian Decision Theory
- 6.5.1 Dempster-Shafer-Bayesian equivalence example
- 6.5.2 Dempster-Shafer-Bayesian computation time comparisons
- 6.6 Developing Probability Mass Functions
- 6.6.1 Probability masses derived from known characteristics of sensor data
- 6.6.1.1 IR sensor probability mass functions
- 6.6.1.2 Metal detector probability mass functions
- 6.6.1.3 Ground-penetrating radar probability mass functions
- 6.6.1.4 Probability mass functions from sensor combinations
- 6.6.2 Probability masses derived from confusion matrices
- 6.6.2.1 Formation of travel time hypotheses
- 6.6.2.2 Confusion matrices
- 6.6.2.3 Computing probability mass functions
- 6.6.2.4 Combining probability masses for a selected hypothesis
- 6.7 Probabilistic Models for Transformation of Dempster-Shafer Belief Functions for Decision Making
- 6.7.1 Pignistic transferable belief model
- 6.7.2 Plausibility transformation function
- 6.7.3 Combat identification example
- 6.7.3.1 Belief
- 6.7.3.2 Plausibility
- 6.7.3.3 Plausibility probability
- 6.7.3.4 Pignistic probability
- 6.7.4 Modified Dempster-Shafer rule of combination
- 6.7.5 Plausible and paradoxical reasoning
- 6.7.5.1 Proposed solution
- 6.7.5.2 Resolution of the medical diagnosis dilemma
- 6.8 Summary
- References
**Chapter 7. Artificial Neural Networks**- 7.1 Applications of Artificial Neural Networks
- 7.2 Adaptive Linear Combiner
- 7.3 Linear Classifiers
- 7.4 Capacity of Linear Classifiers
- 7.5 Nonlinear Classifiers
- 7.5.1 Madaline
- 7.5.2 Feedforward network
- 7.6 Capacity of Nonlinear Classifiers
- 7.7 Generalization
- 7.7.1 Hamming distance firing rule
- 7.7.2 Training set size for valid generalization
- 7.8 Supervised and Unsupervised Learning
- 7.9 Supervised Learning Rules
- 7.9.1 u-LMS steepest descent algorithm
- 7.9.2 a-LMS error correction algorithm
- 7.9.3 Comparison of the u-LMS and a-LMS algorithms
- 7.9.4 Madaline I and II error correction rules
- 7.9.5 Perceptron rule
- 7.9.6 Backpropagation algorithm
- 7.9.6.1 Training process
- 7.9.6.2 Initial conditions
- 7.9.6.3 Normalization of input and output vectors
- 7.9.7 Madaline III steepest descent rule
- 7.9.8 Dead zone algorithms
- 7.10 Other Artificial Neural Networks and Data Fusion Techniques
- 7.11 Summary
- References
**Chapter 8. Voting Logic Fusion**- 8.1 Sensor Target Reports
- 8.2 Sensor Detection Space
- 8.2.1 Venn diagram representation of detection space
- 8.2.2 Confidence levels
- 8.2.3 Detection modes
- 8.3 System Detection Probability
- 8.3.1 Derivation of system detection and false-alarm probability for nonnested confidence levels
- 8.3.2 Relation of confidence levels to detection and false-alarm probabilities
- 8.3.3 Evaluation of conditional probability
- 8.3.4 Establishing false-alarm probability
- 8.3.5 Calculating system detection probability
- 8.3.6 Summary of detection probability computation model
- 8.4 Application Example without Singleton-Sensor Detection Modes
- 8.4.1 Satisfying the false-alarm probability requirement
- 8.4.2 Satisfying the detection probability requirement
- 8.4.3 Observations
- 8.5 Hardware Implementation of Voting Logic Sensor Fusion
- 8.6 Application with Singleton-Sensor Detection Modes
- 8.7 Comparison of Voting Logic Fusion with Dempster-Shafer Evidential Theory
- 8.8 Summary
- References
**Chapter 9. Fuzzy Logic and Fuzzy Neural Networks**- 9.1 Conditions under which Fuzzy Logic Provides an Appropriate Solution
- 9.2 Fuzzy Logic Application to an Automobile Antilock Braking System
- 9.3 Basic Elements of a Fuzzy System
- 9.3.1 Fuzzy sets
- 9.3.2 Membership functions
- 9.3.3 Effect of membership function widths on control
- 9.3.4 Production rules
- 9.4 Fuzzy Logic Processing
- 9.5 Fuzzy Centroid Calculation
- 9.6 Balancing an Inverted Pendulum with Fuzzy Logic Control
- 9.6.1 Conventional mathematical solution
- 9.6.2 Fuzzy logic solution
- 9.7 Fuzzy Logic Applied to Multi-Target Tracking
- 9.7.1 Conventional Kalman-filter approach
- 9.7.2 Fuzzy Kalman-filter approach
- 9.8 Scene Classification Using Bayesian Classifiers and Fuzzy Logic
- 9.9 Fusion of Fuzzy-Valued Information from Multiple Sources
- 9.10 Fuzzy Neural Networks
- 9.11 Summary
- References
**Chapter 10. Data Fusion Issues Associated With Multiple-Radar Tracking Systems**- 10.1 Measurements and Tracks
- 10.2 Radar Trackers
- 10.2.1 Tracker performance parameters
- 10.2.2 Radar tracker design issues
- 10.3 Sensor Registration
- 10.3.1 Sources of registration error
- 10.3.2 Effects of registration errors
- 10.3.3 Registration requirements
- 10.4 Coordinate Conversion
- 10.4.1 Stereographic coordinates
- 10.4.2 Conversion of radar measurements into system stereographic coordinates
- 10.5 General Principle of Estimation
- 10.6 Kalman Filtering
- 10.6.1 Application to radar tracking
- 10.6.2 State transition model
- 10.6.3 Measurement model
- 10.6.3.1 Cartesian stereographic coordinates
- 10.6.3.2 Spherical stereographic coordinates
- 10.6.3.3 Object in straight-line motion
- 10.6.4 The discrete-time Kalman Filter algorithm
- 10.6.5 Relation of measurement-to-track correlation decision to the Kalman gain
- 10.6.6 Initialization and subsequent recursive operation of the filter
- 10.6.7 a-b filter
- 10.6.8 Kalman gain modification methods
- 10.6.9 Noise covariance values and filter tuning
- 10.6.10 Process noise model for tracking manned aircraft
- 10.6.11 Constant velocity target kinematic model process noise
- 10.6.12 Constant acceleration target kinematic model process noise
- 10.7 Extended Kalman Filter
- 10.8 Track Initiation in Clutter
- 10.8.1 Sequential probability ratio test
- 10.8.2 Track initiation recommendations
- 10.9 Interacting Multiple Models
- 10.9.1 Applications
- 10.9.2 IMM implementation
- 10.9.3 Two-model IMM example
- 10.10 Impact of Fusion Process Location and Data on Multiple-Radar State Estimation Architectures
- 10.10.1 Centralized measurement processing
- 10.10.2 Centralized track processing using single-radar tracking
- 10.10.3 Distributed measurement processing
- 10.10.4 Distributed track processing using single-radar tracking
- 10.11 Summary
- References
**Chapter 11. Passive Data Association Techniques for Unambiguous Location of Targets**- 11.1 Data Fusion Options
- 11.2 Received-Signal Fusion
- 11.2.1 Coherent processing technique
- 11.2.2 System design issues
- 11.3 Angle-Data Fusion
- 11.3.1 Solution space for emitter locations
- 11.3.2 Zero-one integer programming algorithm development
- 11.3.3 Relaxation algorithm development
- 11.4 Decentralized Fusion Architecture
- 11.4.1 Local optimization of direction angle track association
- 11.4.2 Global optimization of direction angle track association
- 11.4.2.1 Closest approach distance metric
- 11.4.2.2 Hinge angle metric
- 11.5 Passive Computation of Range Using Tracks from a Single-Sensor Site
- 11.6 Summary
- References
**Chapter 12. Retrospective Comments**- 12.1 Maturity of Data Fusion
- 12.2 Fusion Algorithm Selection
- 12.3 Prerequisites for Using Level 1 Object Refinement Algorithms
**Appendix A. Planck Radiation Law and Radiative Transfer****Appendix B. Voting Fusion With Nested Confidence Levels****Appendix C. The Fundamental Matrix of a Fixed Continuous-Time System**

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