Muutke küpsiste eelistusi

E-raamat: Handbook of Multisensor Data Fusion: Theory and Practice, Second Edition 2nd edition [Taylor & Francis e-raamat]

Edited by , Edited by (Pennsylvania State University, College of Information Sciences and Technology, University Park, USA), Edited by (State University of New York, Buffalo, USA)
Teised raamatud teemal:
  • Taylor & Francis e-raamat
  • Hind: 327,75 €*
  • * hind, mis tagab piiramatu üheaegsete kasutajate arvuga ligipääsu piiramatuks ajaks
  • Tavahind: 468,21 €
  • Säästad 30%
Handbook of Multisensor Data Fusion: Theory and Practice, Second Edition 2nd editionSuurem pilt
Teised raamatud teemal:
Taylor & Francis e-raamatudRohkem infot Taylor & Francis e-raamatute kohta
Raamatu kodulehekülg: https://www.taylorfrancis.com/books/9781315219486
In the years since the bestselling first edition, fusion research and applications have adapted to service-oriented architectures and pushed the boundaries of situational modeling in human behavior, expanding into fields such as chemical and biological sensing, crisis management, and intelligent buildings.

Handbook of Multisensor Data Fusion: Theory and Practice, Second Edition represents the most current concepts and theory as information fusion expands into the realm of network-centric architectures. It reflects new developments in distributed and detection fusion, situation and impact awareness in complex applications, and human cognitive concepts. With contributions from the worlds leading fusion experts, this second edition expands to 31 chapters covering the fundamental theory and cutting-edge developments that are driving this field.

New to the Second Edition

· Applications in electromagnetic systems and chemical and biological sensors



· Army command and combat identification techniques

· Techniques for automated reasoning

· Advances in Kalman filtering

· Fusion in a network centric environment

· Service-oriented architecture concepts

· Intelligent agents for improved decision making

· Commercial off-the-shelf (COTS) software tools

From basic information to state-of-the-art theories, this second edition continues to be a unique, comprehensive, and up-to-date resource for data fusion systems designers.
Preface xiii
Acknowledgment xv
Editors xvii
Contributors xix
Multisensor Data Fusion
1(14)
David L. Hall
James Llinas
Introduction
1(1)
Multisensor Advantages
2(1)
Military Applications
3(2)
Nonmilitary Applications
5(2)
Three Processing Architectures
7(1)
Data Fusion Process Model
8(2)
Assessment of the State-of-the-Art
10(1)
Dirty Secrets in Data Fusion
11(2)
Additional Information
13(2)
References
13(2)
Data Fusion Perspectives and Its Role in Information Processing
15(30)
Otto Kessler
Frank White
Operational Perspective of Fusion
16(6)
Introduction: Fusion in Command and Control and Decision Processes
16(1)
History of Fusion in Operations
17(1)
Automation of Fusion Processes in Operation
18(1)
Automation of Fusion in Operations, the SOSUS Experience
19(2)
Operational Fusion Perspectives
21(1)
Fusion as an Element of Information Process
21(1)
Data Fusion in the Information-Processing Cycle
22(11)
Functional Model of Data Fusion
23(1)
Joint Directors of Laboratories
23(1)
JDL Data Fusion Subpanel and the JDL Model
24(1)
Use of JDL Model
25(1)
Information-Processing Cycle
26(2)
Data Fusion in the Information-Processing Cycle
28(1)
Resource Management in the Information-Processing Cycle
28(2)
Data Mining in the Information-Processing Cycle
30(2)
Human Role
32(1)
Challenges of Net-Centricity
33(4)
Motivation
33(1)
Net-Centric Environment
34(2)
Implications for Fusion
36(1)
Control Paradigm: TRIP Model Implications for Resource Management and Data Fusion
37(6)
Resource-Management Model
38(1)
TRIP Model
38(3)
Coupling Resource Management with Data Fusion
41(1)
Motivational Note
42(1)
References
43(2)
Revisions to the JDL Data Fusion Model
45(24)
Alan N. Steinberg
Christopher L. Bowman
Objective
45(6)
Background
46(1)
Role of Data Fusion
46(1)
1998 Revision
47(2)
Definition of Data Fusion
49(1)
Motivation for Present Revision
50(1)
Recommended Refined Definitions of Data Fusion Levels
51(1)
Discussion of Data Fusion Levels
52(4)
Signal/Feature Assessment
52(1)
Entity Assessment
53(1)
Situation Assessment
54(1)
Impact Assessment
55(1)
Process Assessment
55(1)
Information Flow Within and Across the ``Level''
56(2)
Model Extensions and Variants
58(3)
User Refinement
58(1)
Dasarathy's Input/Output Model
59(2)
Other Data Fusion Models
61(1)
Data Fusion and Resource Management Levels
61(4)
Data Fusion and Resource Management Processing Level Issues
65(1)
References
66(3)
Introduction to the Algrithmics of Data Association in Multiple-Target Tracking
69(20)
Jeffrey K. Uhlmann
Introducition
69(10)
Keeping Track
70(2)
Nearest Neighbors
72(2)
Track Splitting and Multiple Hypotheses
74(1)
Gating
75(2)
Binary Search and kd-Trees
77(2)
Ternary Trees
79(3)
Priority kd-Trees
82(5)
Applying the Results
84(3)
Conclusion
87(1)
Acknowledgment
87(1)
References
87(2)
Principles and Practice of Image and Spatial Data Fusion
89(26)
Ed Waltz
Tim Waltz
Introduction
89(1)
Motivations for Combining Image and Spatial Data
90(2)
Defining Image and Spatial Data Fusion
92(3)
Three Classic Levels of Combination for Multisensor Automatic Target Recognition Data Fusion
95(5)
Pixel-Level Fusion
95(1)
Feature-Level Fusion
96(1)
Discrete Model Matching Approach
97(1)
Adaptive Model Matching Approach
97(1)
Decision-Level Fusion
98(1)
Multiple-Level Fusion
99(1)
Image Data Fusion for Enhancement of Imagery Data
100(2)
Multiresolution Imagery
100(1)
Dynamic Imagery
100(1)
Three-Dimensional Imagery
101(1)
Spatial Data Fusion: Applications
102(6)
Spatial Data Fusion: Combining Image and Nonimage Data to Create Spatial Information Systems
102(1)
Mapping, Charting, and Geodesy Applications
103(2)
Representative Military Example
105(1)
Representative Crime Mapping Examples
106(2)
Spatial Data Fusion in GEOINT
108(2)
Summary
110(1)
References
111(4)
Data Registration
115(22)
Richard R. Brooks
Lynne Grewe
Introduction
115(1)
Registration Problem
116(1)
Review of Existing Research
117(5)
Registration Using Meta-Heuristics
122(2)
Wavelet-Based Registration of Range Images
124(3)
Registration Assistance/Preprocessing
127(2)
Registration Using Elastic Transformations
129(1)
Multimodal Image Registration
130(2)
Theoretical Bounds
132(1)
Conclusion
133(4)
Acknowledgments
134(1)
References
134(3)
Data Fusion Automation: A Top-Down Perspective
137(28)
Richard Antony
Introduction
137(8)
Biological Fusion Metaphor
138(1)
Puzzle-Solving Metaphor
139(3)
Command and Control Metaphor
142(1)
Evidence Combination
143(1)
Information Requirements
143(1)
Problem Dimensionality
144(1)
Commensurate and Noncommensurate Data
145(1)
Biologically Motivated Fusion Process Model
145(7)
Fusion Process Model Extensions
152(8)
All-Source
154(1)
Entity Tracking
154(2)
Track Coincidence
156(1)
Key Locations
156(1)
Behavior
156(1)
Context
157(2)
HUMINT and the JDL Fusion Model
159(1)
Context Support Extensions
159(1)
Observations
160(2)
Observation 1
160(1)
Observation 2
160(1)
Observation 3
160(1)
Observation 4
161(1)
Observation 5
162(1)
Acknowledgment
162(1)
References
163(2)
Overview of Distributed Decision Fusion
165(12)
Martin E. Liggins
Introduction
165(1)
Single Node Detection Fundamentals
166(2)
Parallel Fusion Network
168(3)
Optimizing Local Decision Managers (Step 1)
169(1)
Optimizing Fusion Rules (Step 2)
170(1)
Fusion rules
171(4)
Summary
175(1)
References
175(2)
Introduction to Particle Filtering: The Next Stage in Tracking
177(26)
Martin E. Liggins
Kuo-Chu Chang
Introduction
177(1)
Target State Filtering Problem
178(6)
Chapman-Komolgorov Equation
179(3)
Monte Carlo Integration and Importance Sampling
182(2)
Particle Filter
184(1)
Resampling
185(2)
Markov Chain Monte Carlo
187(2)
Metropolis-Hastings
189(1)
Particle Filtering Example
190(1)
Provide a Set of Performance Evaluations
191(8)
Summary
199(1)
References
200(3)
Target Tracking Using Probabilistic Data Association-Based Techniques with Applications to Sonar, Radar, and EO Sensors
203(40)
T. Kirubarajan
Yaakov Bar-Shalom
Introduction
204(1)
Probabilistic Data Association
205(5)
Assumptions
205(1)
PDAF Approach
205(1)
Measurement Validation
206(1)
State Estimation
206(1)
State and Covariance Update
207(1)
Prediction Equations
208(1)
Probabilistic Data Association
208(2)
Parametric PDA
210(1)
Nonparametric PDA
210(1)
Low Observable TMA Using the ML-PDA Approach with Features
210(10)
Amplitude Information Feature
210(2)
Target Models
212(2)
Maximum Likelihood Estimator Combined with PDA: the ML-PDA
214(2)
Cramer-Rao Lower Bound for the Estimate
216(2)
Results
218(2)
IMMPDAF for Tracking Maneuvering Targets
220(10)
Coordinate Selection
221(1)
Track Formation
222(1)
Track Maintenance
223(1)
Probabilistic Data Association
224(2)
IMM Estimator Combined with PDA Technique
226(2)
Models in the IMM Estimator
228(1)
Track Termination
229(1)
Simulation Results
229(1)
Flexible-Window ML-PDA Estimator for Tracking Low Observable Targets
230(10)
Scenario
231(1)
Formulation of ML-PDA Estimator
231(1)
Target Models
232(2)
Maximum Likelihood-Probabilistic Data Association Estimator
234(2)
Adaptive ML-PDA
236(1)
Results
237(1)
Estimation Results
237(3)
Computational Load
240(1)
Summary
240(1)
References
241(2)
Introduction to the Combinatorics of Optimal and Approximate Data Association
243(22)
Jeffrey K. Uhlmann
Introduction
243(1)
Background
244(2)
Most Probable Assignments
246(1)
Optimal Approach
247(2)
Computational Considerations
249(1)
Efficient Computation of Joint Assignment Matrix
250(2)
Crude Permanent Approximations
252(1)
Approximations Based on Permanent Inequalities
253(2)
Comparisons of Different Approaches
255(3)
Large-Scale Data Association
258(3)
Generalizations
261(1)
Conclusions
261(1)
Acknowledgments
262(1)
Appendix: Algorithm for Data Association Experiment
262(1)
Refrences
263(2)
Bayesian Approach to Multiple-Target Tracking
265(34)
Lawrence D. Stone
Introduction
266(2)
Definition of Bayesian Approach
267(1)
Relationship to Kalman Filtering
267(1)
Bayesian Formulation of Single-Target Tracking Problem
268(6)
Bayesian Filtering
268(1)
Problem Definition
268(1)
Target State Space
268(1)
Prior Information
269(1)
Sensors
269(1)
Likelihood Functions
269(1)
Posterior
270(1)
Computing the Posterior
270(1)
Recursive Method
270(1)
Single-Target Recursion
271(1)
Likelihood Functions
271(1)
Line-of-Bearing Plus Detection Likelihood Functions
272(1)
Combining Information Using Likelihood Functions
273(1)
Multiple-Target Tracking without Contacts or Association (Unified Tracking)
274(4)
Multiple-Target Motion Model
274(1)
Multiple-Target Motion Process
275(1)
Multiple-Target Likelihood Functions
275(1)
Posterior Distribution
276(1)
Unified Tracking Recursion
276(1)
Multiple-Target Tracking without Contacts or Association
277(1)
Summary of Assumptions for Unified Tracking Recursion
278(1)
Multiple-Hypothesis Tracking
278(10)
Contacts, Scans, and Association Hypotheses
279(1)
Contacts
279(1)
Scans
279(1)
Data Association Hypotheses
279(1)
Scan Association Hypotheses
280(1)
Scan and Data Association Likelihood Functions
280(1)
Scan Association Likelihood Function
280(2)
Data Association Likelihood Function
282(1)
General Multiple-Hypothesis Tracking
282(1)
Conditional Target Distributions
283(1)
Associations Probabilities
283(1)
General MHT Recursion
284(1)
Summary of Assumptions for General MHT Recursion
284(1)
Independent Multiple-Hypothesis Tracking
285(1)
Conditionally Independent Scan Association Likelihood Functions
285(2)
Independent MHT Recursion
287(1)
Relationship of Unified Tracking to MHT and Other Tracking Approaches
288(1)
General MHT Is a Special Case of Unified Tracking
288(1)
Relationship of Unified Tracking to Other Multiple-Target Tracking Algorithms
288(1)
Critique of Unified Tracking
289(1)
Likelihood Ratio Detection and Tracking
289(8)
Basic Definitions and Relations
290(1)
Likelihood Ratio
291(1)
Measurement Likelihood Ratio
291(1)
Likelihood Ratio Recursion
292(1)
Simplified Recursion
292(2)
Log-Likelihood Ratios
294(1)
Declaring a Target Present
295(1)
Minimizing Bayes' Risk
295(1)
Target Declaration at a Given Confidence Level
296(1)
Neyman-Pearson Criterion for Declaration
296(1)
Track-Before-Detect
296(1)
References
297(2)
Data Association Using Multiple-Frame Assignments
299(46)
Aubrey B. Poore
Suihun Lu
Brian J. Suchomel
Introduction
299(2)
Problem Background
301(1)
Assignment Formulation of Some General Data Association Problems
302(4)
Multiple-Frame Track Initiation and Track Maintenance
306(3)
Track Initiation
306(1)
Track Maintenance Using a Sliding Window
307(1)
Single-Pane Sliding Window
307(1)
Double-and Multiple-Pane Window
308(1)
Algorithms
309(5)
Preprocessing
309(1)
Fine Gating
309(1)
Problem Decomposition
310(1)
Lagrangian Relaxation Algorithm for the Assignment Problem
311(2)
Class of Algorithms
313(1)
Algorithm Complexity
314(1)
Improvement Methods
314(1)
Future Directions
314(2)
Other Data Association Problems and Formulations
314(1)
Frames of Data
314(1)
Sliding Windows
315(1)
Algorithms
315(1)
Network-Centric Multiple-Frame Assignments
316(1)
Acknowledgments
316(1)
References
316(3)
Introduction
319(1)
Decentralized Data Fusion
320(3)
Covariance Intersection
323(4)
Problem Statement
323(1)
Covariance Intersection Algorithm
324(3)
Using Covariance Intersection for Distributed Data Fusion
327(2)
Extended Example
329(4)
Incorporating Known-Independent Information
333(6)
Example Revisited
336(3)
Conclusions
339(1)
Acknowledgments
340(1)
Appendix 14.A Consistency of CI
340(1)
Appendix 14.B MATLAB Source Code
341(1)
Conventional CI
341(1)
Split CI
342(1)
References
342(3)
General Decentralized Data Fusion with Covariance Intersection
345(24)
Simon Julier
Jeffrey K. Uhlmann
Introduction
345(1)
Estimation in Nonlinear Systems
346(3)
Problem Statement
346(2)
Transformation of Uncertainty
348(1)
Unscented Transformation
349(3)
Basic Idea
349(2)
Example Set of Sigma Points
351(1)
Properties of the Unscented Transform
352(1)
Unscented Transformation
352(4)
Polar to Cartesian Coordinates
353(1)
Discontinuous Transformation
354(2)
Unscented Filter
356(2)
Case Study: Using the UF with Linearization Errors
358(3)
Case Study: Using the UF with a High-Order Nonlinear System
361(2)
Multilevel Sensor Fusion
363(3)
Conclusions
366(1)
Acknowledgments
366(1)
References
366(3)
Random Set Theory for Multisource-Multitarget Information Fusion
369(42)
Ronald Mahler
Introduction
371(7)
Bayesian Iceberg: Models, Optimality, Computability
372(1)
Bayesian Iceberg: Sensor Models
373(1)
Bayesian Iceberg: Motion Models
373(1)
Bayesian Iceberg: State Estimation
374(1)
Bayesian Iceberg: Formal Optimality
374(1)
Bayesian Iceberg: Computability
374(1)
Bayesian Iceberg: Robustness
374(1)
Why Multisource, Multitarget, Multievidence Problems Are Tricky
375(1)
Finite-Set Statistics
375(2)
Why Random Sets?
377(1)
Review of Bayes Filtering and Estimation
378(2)
Bayes Recursive Filtering
378(1)
Constructing Likelihood Functions from Sensor Models
379(1)
Constructing Markov Densities from Motion Models
380(1)
Optimal State Estimators
380(1)
Extension to Nontraditional Data
380(10)
General Data Modeling
382(1)
Generalized Measurements
383(1)
Random Set Uncertainty Models
384(1)
Vague Measurements: Fuzzy Logic
384(1)
Uncertain Measurements: Dempster-Shafer Evidence
384(1)
Contingent Measurements: Rules
384(1)
Unambiguously Generated Ambiguous Measurements
385(1)
Generalized Likelihood Functions for UGA Measurements
385(1)
Bayesian Unfication of UGA Measurement Fusion
386(1)
Bayes-Invariant Transformations of UGA Measurements
387(1)
Ambiguously Generated Ambiguous Measurements
388(1)
Generalized Likelihood Functions for AGM Measurements
388(1)
Ambiguously Generated Unambiguous Measurements
389(1)
Generalized State-Estimates
389(1)
Unified Single-Target Multisource Integration
390(1)
Multisource-Multitarget Calculus
390(5)
Random Finite Sets
391(1)
Multiobject Density Functions and Set Integrals
391(1)
Belief-Mass Functions
391(1)
Probability Generating Functionals
392(1)
Functional Derivatives and Set Derivatives
392(1)
Key Theorems of Multitarget Calculus
393(1)
Fundamental Theorem of Multitarget Calculus
394(1)
Radon-Nikodym Theorem for Multitarget Calculus
394(1)
Fundamental Convolution Formula for Multitarget Calculus
394(1)
Basic Differentiation Rules
394(1)
Multitarget Likelihood Functions
395(2)
Multitarget Measurement Models
395(1)
Case I: No Missed Detections, No False Alarms
395(1)
Case II: Missed Detections
395(1)
Case III: Missed Detections and False Alarms
396(1)
Case IV: Multiple Sensors
396(1)
Belief-Mass Functions of Multitarget Sensor Models
396(1)
Constructing True Multitarget Likelihood Functions
397(1)
Multitarget Markov Densities
397(3)
Multitarget Motion Models
398(1)
Case I: Target Number Is Constant
398(1)
Case II: Target Number Can Decrease
398(1)
Case III: Target Number Can Increase and Decrease
399(1)
Belief-Mass Functions of Multitarget Motion Models
399(1)
Constructing True Multitarget Markov Densities
399(1)
Multisource-Multitarget Bayes Filter
400(3)
Multisensor-Multitarget Filter Equations
400(1)
Initialization
400(1)
Multitarget Distributions and Units of Measurement
401(1)
Failure of the Classical State Estimators
401(1)
Optimal Multitarget State Estimators
402(1)
Multitarget Miss Distance
402(1)
Unified Multitarget Multisource Integration
403(1)
PHD and CPHD Filters
403(3)
Probability Hypothesis Density
404(1)
PHD Filter
404(1)
Cardinalized PHD Filter
405(1)
Survey of PHD/CPHD Filter Research
405(1)
Summary and Conclusions
406(1)
Acknowledgments
407(1)
References
407(4)
Distributed Fusion Architectures, Algorithms, and Performance within a Network-Centric Architecture
411(26)
Martin E. Liggins
Kuo-Chu Chang
Introduction
411(2)
Distributed Fusion within a Network-Centric Environment
413(4)
Information Graph
417(4)
Centralized Architecture
417(1)
Hierarchical Architecture without Feedback
418(1)
Hierarchical Architecture with Feedback
419(1)
Distributed Architecture
420(1)
Fusion Algorithm and Distributed Estimation
421(3)
Distributed Fusion Algorithms
424(5)
Naive Fusion
424(1)
Cross-Covariance Fusion
425(1)
Information Matrix Fusion
426(1)
Maximum A Posteriori Fusion
426(1)
Covariance Intersection (CI) Fusion
427(2)
Performance Evaluation Between Fusion Techniques
429(4)
Fusion without Feedback
431(1)
Fusion with Feedback
432(1)
Hierarchical Fusion with Partial Feedback
433(1)
Summary
433(1)
References
433(4)
Foundations of Situation and Threat Assessment
437(66)
Alan N. Steinberg
Scope and Definitions
438(10)
Definition of Situation Assessement
438(2)
Definition of Threat Assessment
440(2)
Inference in Situation and Threat Assessment
442(1)
Inferences of Relationships and Entity States
443(2)
Inferring Situations
445(2)
Issues in Situation and Threat Assessment
447(1)
Models of Situation Assessment
448(12)
Situation Assessment in the JDL Data Fusion Model
448(2)
Endsley's Model for Situation Awareness
450(2)
Salerno's Model for Higher-Level Fusion
452(1)
Situation Theory and Logic
453(1)
Classical (Deterministic) Situation Logic
453(2)
Dealing with Uncertainty
455(2)
State Transition Data Fusion Model
457(3)
Ontology for Situation and Threat Assessment
460(6)
Ontology Specification Languages
462(1)
Ontologies for Situation Threat Assessment
463(1)
Core Situation Awareness Ontology
463(1)
Ontology of Threat and Vulnerability
464(2)
A Model for Threat Assessment
466(6)
Threat Model
467(2)
Models of Human Response
469(3)
System Engineering for Situation and Threat Assessment
472(24)
Data Fusion for Situation and Threat Assessment
472(1)
Data Fusion Node for Situation and Threat Assessment
472(1)
Architecture Implications for Adaptive Situation Threat Assessment
473(1)
Data Alignment in Situation and Threat Assessment
474(1)
Semantic Registration: Semantics and Ontologies
474(1)
Confidence Normalization
475(5)
Data Association in Situation and Threat Assessment
480(2)
State Estimation in Situation and Threat Assessment
482(1)
Link Analysis Methods
483(1)
Graph Matching Methods
484(1)
Template Methods
485(1)
Belief Networks
486(4)
Compositional Methods
490(3)
Algorithmic Techniques for Situation and Threat Assessment
493(1)
Data Management
493(1)
Hypothesis Structure Issues
493(2)
Data Repository Structure Issues
495(1)
Summary
496(1)
References
496(7)
Introduction to Level 5 Fusion: The Role of the User
503(34)
Erik Blasch
Introduction
503(1)
User Refinement in Information Fusion Design
504(6)
User Roles
504(3)
Prioritization of Needs
507(1)
Contextual Information
508(1)
Cognitive Fusion
509(1)
Data Fusion Information Group Model
510(4)
Sensor Management
512(1)
User Interaction with Design
513(1)
User Refinement
514(6)
User Action
515(2)
User Control
517(1)
User Interaction with Estimation
518(2)
User-Refinement Issues
520(9)
User Models in Situational Awareness
520(2)
Metrics
522(1)
User Evaluation
522(1)
Cognitive Processing in Dynamic Decision Making
523(3)
Cognitive Work Analysis/Task Analysis
526(1)
Display/Interface Design
527(2)
Example: Assisted Target Identification through User-Algorithm Fusion
529(1)
Summary
530(3)
References
533(4)
Perspectives on the Human Side of Data Fusion: Prospects for Improved Effectiveness Using Advanced Human-Computer Interfaces
537(12)
David L. Hall
Cristin M. Hall
Sonya A. H. McMullen
Introduction
537(2)
Enabling Human-Computer Interface Technologies
539(4)
Three-Dimensional Visualization Techniques/Environments
539(3)
Sonification
542(1)
Haptic Interfaces
543(1)
The Way Ahead: Recommendations for New Research Directions
543(3)
Innovative Human-Computer Interface Designs
543(1)
Cross-Fertilization
543(1)
Mining the Abnormal
544(1)
Quantitative Experiments
544(1)
Multisensory Experiments
545(1)
References
546(3)
Requirements Derivation for Data Fusion Systems
549(12)
Ed Waltz
David L. Hall
Introduction
549(1)
Requirements Analysis Process
550(2)
Engineering Flow-Down Approach
552(1)
Enterprise Architecture Approach
553(3)
The Three Views of the Enterprise Architecture
554(2)
Comparison of Approaches
556(1)
Requirements for Data Fusion Services
557(2)
References
559(2)
Systems Engineering Approach for Implementing Data Fusion Systems
561(36)
Christopher L. Bowman
Alan N. Steinberg
Scope
561(2)
Architecture for Data Fusion
563(18)
Role of Data Fusion in Information Processing Systems
563(2)
The Role for the DNN Architecture
565(3)
Components of the DNN Technical Architecture for DF&RM System Development
568(7)
DF&RM Node Processing
575(1)
Data Fusion Node Processing
575(2)
Resource Management Node Processing
577(3)
Comparison of the Dual Data Association and Response Planning Functions at Each DF&RM Level
580(1)
Data Fusion System Engineering Process
581(3)
Sample Applications of the DF&RM DNN Architecture
584(7)
Level 2 Fusion DNN Application Example
584(1)
Level 3 Fusion DNN Application Example
585(1)
Level 4 DF&RM DNN Application Example
585(2)
Dual RM DNN Application Example
587(2)
DF&RM System Engineering as a Level 4 Resource Management Problem
589(2)
The DF&RM Model Unification Provided by the DNN Architecture
591(4)
Dasarathy Fusion Model
591(1)
Bedworth and O'Brien's Omnibus Model
592(1)
The Kovacich Fusion Taxonomy
592(1)
The Endsley Model
593(2)
References
595(2)
Studies and Analyses within Project Correlation: An In-Depth Assessment of Correlation Problems and Solution Techniques
597(22)
James Llinas
Capt. Lori McConnell
Christopher L. Bowman
David L. Hall
Paul Applegate
Introduction
598(1)
Background and Perspectives on This Study Effort
598(1)
A Description of the Data Correlation Problem
599(2)
Hypothesis Generation
601(3)
Characteristics of Hypothesis Generation Problem Space
601(1)
Solution Techniques for Hypothesis Generation
601(1)
Hypothesis Enumeration
601(3)
Identification of Feasible Hypotheses
604(1)
HG Problem Space to Solution Space Map
604(1)
Hypothesis Evaluation
604(4)
Characterization of the HE Problem Space
604(1)
Input Data Characteristics
604(2)
Output Data Characteristics
606(1)
Mapping of the HE Problem Space to HE Solution Techniques
606(2)
Hypothesis Selection
608(8)
The Assignment Problem
609(1)
Comparisons of Hypothesis Selection Techniques
609(2)
2D Versus ND Performance
611(1)
Engineering an HS Solution
611(1)
Deterministic Approaches
612(4)
Summary
616(1)
References
617(2)
Data Management Support to Tactical Data Fusion
619(36)
Richard Antony
Introduction
620(1)
Database Management Systems
620(2)
Spatial, Temporal, and Hierarchical Reasoning
622(4)
Database Design Criteria
626(11)
Intuitive Algorithm Development
626(1)
Efficient Algorithm Performance
626(1)
Data Representation Accuracy
626(1)
Database Performance Efficiency
627(1)
Storage Efficiency
627(1)
Search Efficiency
628(1)
Overhead Efficiency
628(1)
Association Efficiency
628(1)
Complex Query Efficiency
629(1)
Implementation Efficiency
629(1)
Spatial Data Representation Characteristics
629(5)
Database Design Tradeoffs
634(1)
Object Representation of Space
635(1)
Low-Resolution Spatial Representation
636(1)
High-Resolution Spatial Representation
636(1)
Hybrid Spatial Feature Representation
637(1)
Integrated Spatial/Nonspatial Data Representation
637(2)
Sample Application
639(8)
Problem-Solving Approach
640(2)
Detailed Example
642(5)
Mixed Boolean and Fuzzy Reasoning
647(5)
Introduction
647(3)
Extended Operations
650(2)
Summary and Conclusions
652(1)
Acknowledgments
652(1)
References
653(2)
Assessing the Performance of Multisensor Fusion Processes
655(22)
James Llinas
Introduction
655(2)
Test and Evaluation of the Data Fusion Process
657(5)
Establishing the Context for Evaluation
658(1)
T&E Philosophies
658(1)
T&E Criteria
659(2)
Approach to T&E
661(1)
The T&E Process: A Summary
662(1)
Tools for Evaluation: Testbeds, Simulations, and Standard Data Sets
662(5)
Relating Fusion Performance to Military Effectiveness: Measures of Merit
667(6)
Summary
673(1)
References
674(3)
Survey of COTS Software for Multisensor Data Fusion
677(14)
Sonya A. H. McMullen
Richard R. Sherry
Shikha Miglani
Introduction
677(1)
Taxonomy for Multisensor Data Fusion
678(1)
Survey of COTS Software and Software Environments
678(6)
Special Purpose COTS Software
679(1)
General Purpose Data Fusion Software
679(5)
A Survey of Surveys
684(1)
Discussion
684(3)
References
687(4)
Survey of Multisensor Data Fusion Systems
691(10)
Mary L. Nichols
Introduction
691(1)
Recent Survey of Data Fusion Activities
691(1)
Assessment of System Capabilities
692(9)
References
699(2)
Data Fusion for Developing Predictive Diagnostics for Electromenchanical Systems
701(38)
Carl S. Byington
Amulya K. Garga
Introduction
702(2)
Condition-Based Maintenance Motivation
702(2)
Aspects of a Condition-Based Maintenance System
704(1)
The Diagnosis Problem
704(5)
Feature-Level Fusion
705(1)
Decision-Level Fusion
706(1)
Voting
706(1)
Weighted Decision Fusion
706(1)
Bayesian Inference
707(1)
Model-Based Development
708(1)
Model-Based Identification and Damage Estimation
708(1)
Multisensor Fusion Toolkit
709(1)
Application Examples
710(24)
Mechanical Power Transmission
710(1)
Industrial Gearbox Example
710(9)
Fluid Systems
719(1)
Lubrication System Function
719(2)
Lubrication System Test Bench
721(1)
Turbine Engine Lubrication System Simulation Model and Metasensors
722(1)
Data Fusion Construct
722(1)
Data Analysis Results
723(3)
Health Assessment Example
726(1)
Summary
727(1)
Electrochemical Systems
727(1)
The Battery as a System
727(2)
Mathematical Model
729(1)
Data Fusion of Sensor and Virtual Sensor Data
730(4)
Concluding Remarks
734(1)
Acknowledgments
734(1)
References
735(4)
Adapting Data Fusion to Chemical and Biological Sensors
739(20)
David C. Swanson
Introduction
739(1)
Characterizing the Complexity of Detecting Chemical Agents and Biological Pathogens
740(4)
Chemical Sensors
744(3)
Ion Mobility Spectrometer
744(1)
Surface Acoustic Wave and Electrochemical Cells
745(1)
Flame Photometric Detection
745(1)
Photoionization Detection
745(1)
Spectrographic Methods
746(1)
Colorimetric Sensing
747(1)
Biological Sensors
747(2)
Developing Quantitative and Qualitative Information
749(2)
Inferencing Networks for Heterogeneous Sensor Fusion
751(6)
The Blend Function
753(1)
Qualitative Information Transformation
754(1)
Qualitative Information Transform
755(1)
Concentration Consistency
755(2)
The Path Forward
757(1)
References
758(1)
Fusion of Ground and Satellite Data via Army Battle Command System
759(14)
Stan Aungst
Mark Campbell
Jeff Kuhns
David Beyerle
Todd Bacastow
Jason Knox
Introduction
759(1)
Description of the Army Battle Command System
760(4)
Situational Awareness
760(1)
Common Operating Picture
761(1)
Information Fusion and Decision Making
761(1)
Joint Command and Control
761(1)
The Global Command and Control System-Army
762(1)
Force Battle Command Brigade-and-Below
763(1)
Evolution of the Army Battle Command System
764(2)
Remote Sensing, Ground-Based Systems (Image and Nonimage)
764(1)
Tactical Unmanned Aerial Vehicles and Aerostats (Sensor Networks)
764(2)
Ground Sensors
766(1)
Discussion and Implications for Disaster Management
766(4)
Simulation
769(1)
Intelligence Analysis, Data Mining, and Visualization
770(1)
Summary and Final Recommendations
770(1)
Glossary of Key Terms
771(1)
Acknowledgments
771(1)
References
771(2)
Developing Information Fusion Methods for Combat Identification
773(40)
Tod M. Schuck
J. Bockett Hunter
Daniel D. Wilson
Introduction
774(2)
Mapping CID to JDL Levels
776(8)
Multihypothesis Structures
778(1)
JDL Level 1 Structures
779(4)
JDL Level 2 Structures
783(1)
JDL Level 3 Structures
783(1)
CID Information and Information Theory
784(10)
The Identification System
785(3)
Forming the Identification Vector
788(1)
Choice, Uncertainty, and Entropy for Identification
789(2)
Example of Identification Information Measurement
791(3)
Understanding IFF Sensor Uncertainties
794(3)
Information Properties as a Means to Define CID Fusion Methodologies
797(2)
Fusion Methods
799(3)
Modified Dempster-Shafer Approach
799(1)
Information Fusion Sets
800(1)
Bayesian and Orthodox D-S Results
801(1)
Results and Discussion
802(1)
Multihypothesis Structures and Taxonomies for CID Fusion
802(3)
Taxonomic Relationships Defined
803(1)
Canonical Mappings
804(1)
Response Mapping
805(1)
Multihypothesis Structures, Taxonomies, and Recognition of Tactical Elements for CID Fusion
805(4)
Tactical Elements for CID Fusion
805(1)
CID in SA and Expansion on the JDL Model
805(1)
Recognition of Tactical Elements
806(3)
Conclusions and Future Work
809(4)
References
811(2)
Index 813
Liggins II, Martin; Hall, David; Llinas, James
Püsilink: https://www.kriso.ee/db/9781315219486_pe.html
Märksõnad: