Index
Abnormal cognition and perception, human-computer interaction, 544
Absence of change, top-down data fusion automation, 152–153
Abstracted information features, combat identification (CID) mapping, Shannon communication analysis, 786–788
Access issues, geospatial data fusion, 103
Action information fusion (ACT-IF), information fusion design, 516–517
Adaptive architectures, situation and threat assessment, 473–474
Adaptive filtering, elastic transformations, data registration, 129–130
Adaptive maximum likelihood-probabilistic data association estimator, basic principles, 236–240
Adaptive model matching, automatic target recognition (ATR), feature-level fusion, 97–98
hypothesis enumeration, 603
hypothesis evaluation problem space to solution space mapping, 606–607
Advanced Geospatial Intelligence (AGI) techniques, geospatial intelligence spatial data fusion, 110
Agreement function, combat identification (CID) mapping, modified Dempster–Shafer method, 799–800
Airborne imagery, image and spatial data fusion, 90–92
Air target tracking, top-down data fusion automation, 161–162
Algorithms. See also Data association algorithmics
covariance intersection, 324–327
database management system design
intuitive algorithms, 626
performance evaluation, 626
multiple-frame assignments, 309–314
complexity, 313
future research issues, 315
improvement, 313
Lagrangian relaxation algorithm, 311–313
problem decomposition, 310–311
multisensor data fusion, 12
multisensor data fusion survey, 692–699
situation and threat assessment, 493
Alignment, image and spatial data fusion, 94
All-source, top-down data fusion automation, 154
Ambiguously generated ambiguous (AGA) measurements
generalized likelihood functions, 388–389
Ambiguously generated unambiguous (AGU) measurements, Bayesian filtering, 380–381
Amino acid sequencing, chemical and biological sensors, complexity analysis, 742–743
Amplitude information, target motion analysis, maximum likelihood-probabilistic data association, 210–212
chemical and biological sensors, concentration consistency, 756–757
distributed decision fusion, global decision rules, 173–175
condition-based maintenance, 707–708
situation and threat assessment, 486–488
combat identification (CID) mapping, 793–794
condition-based maintenance, 707–708
information fusion design, 513–514
information-processing cycle, 30–32
single-target tracking, Bayesian formulation, 269
top-down data fusion automation, puzzle-solving metaphor, 140–142
adaptive architectures, 473–474
centralized-fusion, 301–302, 417–418
data fusion & resource management, information processing systems, 563–564
data fusion systems, network-centric architectures, 333–337, 557–559
distributed data fusion, 417–421
enterprise architectures, 553–556
data fusion & resource management, 62–65
requirements derivation, 555
service-oriented architectures, 12, 33
comparisons and applications, 584–591
Areal-based representations, database management systems, 631–634
Army Battle Command System (ABCS), ground and satellite data fusion
common operating picture, 761
disaster management applications, 766–770
final recommendations and future research issues, 770
Force Battle Command Brigade-and-Below, 763–764
Global Command and Control System-Army, 762–763
glossary of terms, 771
ground sensors, 766
information fusion and decision making, 761
joint command and control, 761
remote sensing, ground-based systems, 764
situational awareness, 760–761
tactical unmanned aerial vehicles and aerostats, 764–765
Army Tactical Command and Control System (ATCCS), Army Battle Command System, 762–763
Arrests data, crime mapping spatial data fusion, 106–107
Artificial intelligence (AI), situation and threat assessment, 460–466
Artificial neural systems, top-down data fusion automation, 161
Assignment algorithms, hypothesis selection, 608–611
Assistance/preprocessing, data registration, 127–129
Assisted target identification, information fusion design, user-algorithm fusion, 529–530
database management system design, efficiency of, 628–629
image and spatial data fusion, 94
by location independent of time, 153
multiple-hypothesis tracking, probabilities, 283–284
multiple-target tracking without, 277–278
Attributes, situation assessment and, 54
Automated fusion technology (AFT), information fusion design, user-algorithm fusion, 529–530
Automatic target recognition (ATR)
image and spatial data fusion, 92
multisensor data fusion, 95–100
situation and threat assessment, 490–493
data fusion, top-down automation
all-source model, 154
biological fusion metaphor, 138–139
biologically motivated process model, 145–152
command and control metaphor, 142–143
commensurate/noncommensurate data, 145
context support extensions, 159–160
evidence combination, 143
information requirements, 143–144
JDL fusion model, HUMINT applications, 159
key locations, 156
problem dimensionality, 144–145
process model extensions, 152–153
puzzle-solving metaphor, 139–142
track coincidence, 156
data fusion operations and, 18–21
Autonomous vehicle location, covariance intersection, distributed data fusion, 339
Autoregressive moving average (ARMA) model, condition-based maintenance, sensor/virtual sensor data, 732
Average number of particles, particle filtering, 194–199
Ballistic motion models, multiple-target tracking research, 85
Batteries, condition-based maintenance, 727–729
Bayesian belief networks (BBN)
combat identification (CID) mapping, entity assessment, 780–783
situation and threat assessment, 486–488
combat identification (CID) mapping, 801–802
condition-based maintenance, 707–708
distributed decision fusion, minimum risk, 168–171
finite-set statistics, 372–374
likelihood ratio detection and tracking, 289–296
basic definitions and relations, 290–291
log-likelihood ratios, 294–295
measurement likelihood ratio, 291–292
Neyman-Pearson declaration criterion, 296
target present declaration, 295–296
track-before-detect, 296
motion model Markov densities, 380
multiple-hypothesis tracking, 278–288
association probabilities, 283
conditional target distributions, 283
conditionally independent scan association likelihood functions, 285–287
contacts, 279
data association hypothesis, 279–280
data association likelihood function, 282
independent recursion, 287–288
scan association likelihood function, 280–281
definitions, 267
no contacts/associations, 277–278
posterior distribution, 276
unified tracking recursion, 276–278
multisource-multitarget filter, 400–403
optimal state estimation, 380
particle filtering, 179
Chapman-Komologorov equation, 179–182
Monte Carlo integration, 182–183
sensor model likelihood functions, 378
single-target tracking, 268–274
information combination, likelihood functions, 273–274
line-of-bearing plus detection likelihood functions, 272–273
posterior distribution, 270
prior information, 269
sensors, 269
Bearing likelihood function, single-target tracking, Bayesian formulation, 272–274
Bedworth/O’Brien’s omnibus model, data fusion & resource management, dual node architecture, 592
Behavior, top-down data fusion automation, 156–157
Belief decision trees, situation and threat assessment, 489–490
Bayesian formula, finite-set statistics, 376
finite-set statistics, 391–392
basic principles, 371
Markov densities, multitarget motion models, 399
multitarget sensor models, 396–397
Belief networks, situation and threat assessment, state estimation, 486–490
Benign motion model, interacting multiple model-probabilistic data association filter (IMMPDAF), 228
Benign urn model, combat identification (CID) mapping, 798
Binary search, multiple-target tracking, 77–79
Bioassays, biological sensors, 748–749
Biological fusion metaphor, top-down data fusion automation, 138–139
Biologically motivated fusion process model, top-down data fusion automation, 145–152
Biological sensors, data fusion adaptation
complexity characteristics, 740–743
concentration consistency, 755–757
future research issues, 757–758
inferencing networks, heterogeneous fusion, 751–753
overview, 739
qualitative information, 749–751, 754–755
quantitative information transformation, 749–751
Biotoxins, biological sensor detection, 747–749
Bipartite matching problem, hypothesis selection, 609
data fusion & resource management, problem solving techniques, 581–584
unscented transform, nonlinear data fusion, 352–354
Black Coral LIVE and MOBILE software, multisensor data fusion, 684
Blend response function, chemical and biological sensors, inferencing protocols, 753–757
Board on Natural Disasters (BOND), Army Battle Command System disaster management assessment, 766–770
automatic target recognition (ATR), pixel-level fusion, 96
contextual knowledge, 158
data-base management systems, 647–652
Bottom-up automation, information fusion design, 513–514
Bridging operations, top-down data fusion automation, 150–152
elastic transformations, data registration, 129–130
multimodal image registration, 131–132
C language routines, multisensor data fusion software, 679–687
Calls for service, crime mapping spatial data fusion, 106–107
Canonical mapping, combat identification (CID), 804–805
Capability, situation and threat assessment models, 467–469
Cardinalized probability high-density (CPHD) filters, multisource-multitarget information fusion, 403–406
Cartesian coordinates, unscented transformation, polar transformation to, 353–354
CASE ATTI software, multisensor data fusion, 683–687
Causal evidence, situation and threat assessment, Bayesian belief networks, 486–488
Causality, situation and threat assessment, 447
CAVE virtual reality system, human-computer interaction, 539–541
Centralized fusion architecture
multiple-frame assignments, 301–302
network-centric distributed data fusion, 417–418
Chapman-Kolmogorov equation, target state filtering, 179–182
Charting applications, spatial data fusion, 103–107
Chemical sensors, data fusion adaptation
colorimetric sensing, 747
complexity characteristics, 740–743
flame photometric detection, 745
ion mobility spectrometer, 744
overview, 739
photoionization detection, 745–746
spectrographic detection, 746–757
surface acoustic wave and electrochemical cells, 745
Choice decomposition, combat identification (CID) mapping, 789–794
Class elements, combat identification (CID) mapping, entity assessment, 780–783
Classical (deterministic) situation logic, situation and threat assessment, 453–454
multiple-target tracking, gating criteria, 76–77
situation and threat assessment, 488
Clutter filter performance measures, multisensor data fusion performance evaluation, 671–673
human-computer interaction, 544–545
information fusion design, 509–510
dynamic decision making, 523–526
work analysis/task analysis, 526–527
resource management and information processing, 30
TRIP resource management and data fusion model, 39
Colony-forming units (CFUs), biological sensors, 747–749
Color normalization (CN) technique, image data fusion enhancement, 100
Colorimetric sensing, chemical sensor analysis, 747
Combat identification (CID), information fusion
Bayesian and orthodox Dempster–Shafer results, 801–802
canonical mappings, 804
choice, uncertainty, and entropy, 789–794
Dempster–Shafer approach, 799–801
future research issues, 809–811
identification system principles, 785–788
identification vector formation, 788–789
IFF sensor uncertainties, 794–797
information properties and methodologies, 797–799
multihypothesis structures, 778–784
multihypothesis taxonomies, 802–805
response mapping, 805
Combination function, image and spatial data fusion, 93
Combinatorial optimization, hypothesis selection, 608–609
Combinatorics, data association algorithms
approximation comparisons, 255–258
crude permanent approximations, 252
future research issues, 261–262
joint assignment matrix computations, 250–251
large-scale data associations, 258–261
permanent inequalities approximations, 253–255
real time applications, 244–246
Combined data, data fusion definitions and, 49–50
Combined team-decision solution, distributed decision fusion, 171–175
Command, control, communication, computation, intelligence, surveillance, and reconnaissance (C4ISR), requirements derivation, data fusion, 555–556
Command and control (C2) decision making
Army Battle Command System, 759–760
joint command and control, 761–762
basic principles, 16
top-down data fusion automation, 142–143
Commensurate/noncommensurate data, top-down data fusion automation, 145
Commercial off the shelf (COTS) software
Army Battle Command System disaster management assessment, 767–770
taxonomy, 678
Common Algebraic Specification Language (CASL), situation and threat assessment, 463
Common Operating Picture (COP), Army Battle Command System, 761
combat identification (CID) mapping, theoretical background, 784–785
network-centric architecture, distributed data fusion model, 416–417
Communications intelligence (COMINT), situation and threat assessment, 475–480
Complex query efficiency, database management system design, 629
chemical and biological sensors, 740–743
multiple-frame assignment algorithm, 314
situation and threat assessment, human response models, 469–470
Complexity index, wavelet-based data registration, range images, 125–127
Composite feature spatial representation, database management systems, 631–634
Compositional methods, state estimation, situation and threat assessment, 482–483, 490–493
Comprehension, Endsley’s situation awareness model, 450
Comprehensive vehicle model, unscented filter, high-order nonlinear systems, 362–363
Computability, Bayesian formula, 374
Computational geometry, spatial representation, database management system design, 632–633
Computational load, maximum likelihood-probabilistic data association estimator, 240
Concentration consistency, chemical and biological sensors, 755–757
Conceptual phase, information fusion design, 506
Conditional algebra event (CAE) techniques, hypothesis evaluation problem space to solution space mapping, 606–607
Conditional target distributions, multiple-hypothesis tracking, 283
Condition-based maintenance (CBM), predictive diagnostics
electrochemical systems, 727–734
ARMA state-of-charge prediction, 732
neural networks state-of-charge prediction, 733–734
sensor/virtual sensor data fusion, 730–734
condition-based maintenance systems, 702–704
decision-level fusion, 706–708
feature-level fusion, 705
mechanical power transmission, 710–719
model-based development, 708–709
multisensor fusion toolkit, 709
overview, 702
future research issues, 734
Conditioning, situation and threat assessment, 488
conventional source code, 341–342
split source code, 342
target declaration, 296
Confidence normalization, situation and threat assessment, 475–480
Confusion class of objects, combat identification (CID) fusion methodologies, 797–798
Consistency, covariance intersection, 340–341
Constant-gain Kalman filter (CGKF), multisource-multitarget information fusion, 403–406
Constructive simulation, Army Battle Command System disaster management assessment, 770
multiple-hypothesis tracking, 279
multiple-target tracking without, 277–278
Context-sensitive individual component motion, situation and threat assessment, 446
Context support extensions, top-down data fusion automation, 159–160
information fusion design, 508–509
multisensor data fusion performance evaluation, 658
top-down data fusion automation, 157–158
Contextual reasoning, top-down data fusion automation, 148–152
random set uncertainty models, 384–385
threat assessment, 442
Continuants, situation and threat assessment, 464–465
Continuous analysis and detection of relational evidence (CADRE), situation and threat assessment, 490–493
Continuous inference network logic algorithm (CINet), chemical and biological sensors, inferencing protocols, 752–757
Continuous wavelet transform, condition-based maintenance, industrial gearbox example, 716–719
data fusion & resource management, dual node architecture, 568–575
situation and threat assessment, 470
Control paradigm, TRIP resource management and data fusion model, 37–43
Control points, data registration, 117–122
Control problem, data fusion & resource management, dual node architecture, 568–575
Convolution formula, finite-set statistics, 394
Cooperative information, combat identification (CID) mapping, Shannon communication analysis, 786–788
Coordinate selection, interacting multiple model-probabilistic data association filter (IMMPDAF), 221–222
Coregistration, multisensor data fusion, 6
Core situation awareness ontology, situation and threat assessment, 463–464
data fusion definitions and, 49–50
information features, combat identification (CID) mapping, 786–788
background and perspectives, 598–599
future research issues, 616
hypothesis evaluation, 604–607
input data characteristics, 604–605
output data characteristics, 606
problem space characterization, 604–606
problem space to solution space mapping, 606–607
hypothesis generation, 601–604
feasible hypotheses identification, 604
problem space to solution space mapping, 604
assignment problem, 609
deterministic approaches, 612–615
technique comparisons, 609–611
two-dimensional vs. N-dimensional performance, 611
combat identification (CID) mapping, 806–809
information fusion design, 524–526
consistency properties, 340–341
future research issues, 339–340
known-independent information, 333–338
distributed data fusion, 327–329
network-centric distributed data fusion, 427–429
nonlinear data fusion, 346
Covariance matrix, multiple-target tracking and, 72
Covariance update, probabilistic data association filter (PDAF), 207
target motion analysis, maximum likelihood-probabilistic data association, 216–217
Crime mapping applications, spatial data fusion, 106–107
Critical mobile targets (CMTs), spatial data fusion, 105–106
CRITICAL_SUPPORT service, requirements derivation, data fusion, 558–559
Cross-covariance fusion, network-centric distributed data fusion, 425
Cross-fertilization, human-computer interaction, 543–544
Crude permanent approximations, combinatorics, 252–253
Currency issues, geospatial data fusion, 103
Damage estimation, predictive diagnostics, 708–709
Dasarathy’s input/output model
data fusion & resource management, dual node architecture, 591–592
revised JDL data fusion model and, 59–61
Data alignment, situation and threat assessment, 474–480
confidence normalization, 475–480
semantic registration, 474–475
approximation comparisons, 255–258
crude permanent approximations, 252
future research issues, 261–262
joint assignment matrix computations, 250–251
large-scale data associations, 258–261
permanent inequalities approximations, 253–255
real time applications, 244–246
condition-based maintenance, data fusion construct, 722
data fusion & resource management, resource node processing, 578–580
assignment formulation, 302–306
complexity, 314
future research issues, 314–316
improvements, 314
Lagrangian relaxation, 311–313
net-centric assignments, 316
sliding window track maintenance, 307–308, 315
multiple-hypothesis tracking, 279–282
binary search and kd-trees, 77–79
future research issues, 87
track splitting and multiple hypotheses, 74–75
probabilistic data association (PDA) techniques, overview, 204–205
situation and threat assessment, 480–482
Database management systems (DBMS)
algorithm efficiency, 626
association efficiency, 628–629
complex query efficiency, 629
data representation accuracy, 626
implementation efficiency, 629
intuitive algorithm development, 626
overhead efficiency, 628
performance efficiency, 627–629
search efficiency, 628
spatial data representation characteristics, 629–634
future research issues, 652
hierarchical path-planning algorithm, 640–641
high-resolution spatial representation, 636
hybrid spatial feature representation, 637
integrated spatial/nonspatial data representation, 637–639
low-resolution spatial representation, 636
mixed Boolean and fuzzy reasoning, 647–652
object-oriented space representation, 635–637
spatial, temporal, and hierarchical reasoning, 622–625
tactical data fusion, overview, 620
top-down path-planning process, 642–647
Data correlation problem, Project Correlation description, 599–601
Data-driven methods, state estimation, situation and threat assessment, 482
Data frames, multiple-frame assignment, 314–315
Data fusion. See also Image and spatial data fusion; Multisensor data fusion
automation of operations, 18–21
command and control decision processes, 16
condition-based maintenance, 722–723
information processing, 21–23, 26–30
JDL definition of, 22–23, 49–50
JDL functional model of, 23–26
Dasarathy’s input/output model, 59–61
impact assessment, 55
information flow, cross-levels, 56–58
recommended definition refinements, 51–52
resource management and, 61–65
resource management processing levels, 65–66
signal/feature assessment, 52–53
situation assessment, 54
network-centric architecture, 33–37
future research issues, 366
multilevel sensor fusion, 363–365
uncertainty transformation, 348–349
high-order nonlinear system, 361–363
unscented transformation, 349–352
discontinuous transformation, 354–356
polar-to-Cartesian coordinates, 353–354
properties, 352
sigma points, 351
operational perspective on, 16–22
comparison of design approaches, 556–557
engineering flow-down approach, 552–553
enterprise architecture approach, 553–556
network-centric architectures, 557–559
requirements analysis process, 550–552
resource-management model, 38, 41–42
situation and threat assessment, 472–474
all-source model, 154
biological fusion metaphor, 138–139
biologically motivated process model, 145–152
command and control metaphor, 142–143
commensurate/noncommensurate data, 145
context support extensions, 159–160
evidence combination, 143
information requirements, 143–144
JDL fusion model, HUMINT applications, 159
key locations, 156
problem dimensionality, 144–145
process model extensions, 152–153
puzzle-solving metaphor, 139–142
track coincidence, 156
TRIP model and control paradigm, 37–43
Data fusion and resource management (DF&RM), systems engineering
architectural comparisons and applications, 584–591
architectural requirements, 563–580
Bedworth and O’Brien’s omnibus model, 592
Dasarathy fusion model, 591–592
data fusion and resource management systems, 568–580
data fusion node processing, 575–577
dual data association/response planning functions comparisons, 580
dual node network architecture, 565–575
dual resource management/dual node network application, 587–589
impact assessment applications, 585
information processing systems, 563–565
Kovacich fusion taxonomy, 592–593
process assessment applications, 585–587, 589–591
resource management node processing, 577–579
situation awareness applications, 584
unification model, DF&RM/DNN architectures, 591–594
Data fusion information group (DFIG) model, information fusion design, 510–514
Data in/features out (DAI/FEO), revised JDL data fusion signal/feature assessment, 53
Data management, situation and threat assessment, 493–496
Army Battle Command System disaster management assessment, 770
data fusion & resource management, dual node architecture, 587–589
information-processing cycle, 30–32
TRIP resource management and data fusion model, 42–43
Data modeling, Bayesian filtering, 381–382
assistance/preprocessing, 127–129
elastic transformation, 129–130
existing research review, 117–122
future research issues, 133
wavelet-based range image registration, 124–127
Data repository structure, situation and threat assessment, 495–496
Data representation accuracy, database management system design, 626–627
Daubechies-4 wavelet transform, wavelet-based data registration, range images, 126–127
Davidon-Fletcher-Powell technique, flexible-window maximum likelihood-probabilistic data association estimator, 235
Decentralized data fusion (DDF), covariance intersection
consistency properties, 340–341
future research issues, 339–340
known-independent information, 333–338
combat identification (CID) mapping, 807–809
condition-based maintenance, industrial gearbox example, 718–719
Decision information fusion (DEC-IF), information fusion design, 516–517
automatic target recognition, 98–99
condition-based maintenance, 706–708
multisensor data fusion, 8
Army Battle Command System, 761
distributed decision fusion, 165–166, 168–171
information fusion design, 510–514
local decision manager optimization, 169–170
single node detection, 166–168
Defense systems, multisensor data fusion, 4–5
Deferred logic, multiple-frame assignments, track maintenance, 307–308
Degeneracy effect, particle filtering resampling, 186–187
combat identification (CID) mapping
identification information measurement, 791–794
multisensor data fusion software, 679–687
unambiguously generated ambiguous (UGA) measurements, 387–388
uncertain measurements, 384
dual data fusion & resource management (DF&RM) models, 65
information fusion design, 506
Detection likelihood functions, single-target tracking, Bayesian formulation, line-of-bearing and, 272–274
Detection probability (Pd), Project Correlation, 599
Detect-to-warn capacity, chemical and biological sensors, 740–743
Determinants, situation and threat assessment, 464–465
Deterministic engineering, hypothesis selection solutions, 612–615
DE tree, multiple-target tracking, 84
Diagnostic evidence, situation and threat assessment, 486–488
Differentiation rules, finite-set statistics, 394
Diffuse priori model, probabilistic data association filter (PDAF), 209
Digital elevation models (DEMs), image data fusion enhancement, 100–101
Dirac delta density, probability hypothesis, 404–406
Direct operating costs (DOC), condition-based maintenance, predictive diagnostics, 703–704
DiRecT system, data registration assistance/preprocessing, 127–129
Disaster management, Army Battle Command System applications, 766–770
intelligence analysis, data mining, and visualization, 770
Discontinuous transformation, unscented transformation, nonlinear data fusion, 354–356
Discrete model matching, automatic target recognition (ATR), feature-level fusion, 97
geospatial data fusion, 103
information fusion design, 509–510
Dissimilar source integration (DSI), combat identification (CID), 794–797
Distributed architecture, network-centric distributed data fusion, 420–421
covariance intersection, 327–329
future research issues, 175
historical background, 165–166
local decision manager optimization, 169–170
centralized architecture, 417–418
communications, 416
covariance intersection fusion, 427–429
cross-covariance fusion, 425
distributed architecture, 420–421
distributed information, 421–424
feedback hierarchical architecture, 419–420
fusion algorithm, 416–417, 421–429
future research issues, 433
information matrix fusion, 426
non-feedback hierarchical architecture, 418–419
overview and basic principles, 411–417
performance evaluation, 429–433
parallel fusion network, 168–171
single node detection fundamentals, 166–168
Distribution estimation, network-centric distributed data fusion, 421–424
biological sensors, 749
chemical and biological sensors, complexity analysis, 743
DNBi Supply Management Solution software, multisensor data fusion, 684
Doctrine-based scenarios, hypothesis enumeration, 603
combat identification (CID), 774–776
top-down data fusion automation, puzzle-solving metaphor, 140–142
Double- and multiple-pane window, multiple-frame assignments, track maintenance, 308
Downstream processing, multisensor data fusion, 12
Drift sensing, information fusion design, 513
Dual data fusion & resource management (DF&RM) models, 61–65
Dual node network (DNN) architecture, data fusion & resource management
Dummy report, multiple-frame assignments, 304
geospatial intelligence spatial data fusion, 108–110
image data fusion enhancement, 100
Dynamic replanning, TRIP resource management and data fusion model, 40
Economic philosophy, multisensor data fusion performance evaluation, 659–661
Elastic transformations, data registration, 129–130
predictive diagnostics, 727–734
ARMA state-of-charge prediction, 732
neural networks state-of-charge prediction, 733–734
sensor/virtual sensor data fusion, 730–734
surface acoustic wave (SAW) analysis, 745
Electrode reaction process, condition-based maintenance, batteries, 728–729
Electromechanical systems, predictive diagnostics
condition-based maintenance systems, 702–704
decision-level fusion, 706–708
feature-level fusion, 705
mechanical power transmission, 710–719
model-based development, 708–709
multisensor fusion toolkit, 709
overview, 702
Electronic countermeasures (ECM)
interacting multiple model-probabilistic data association filter (IMMPDAF), 220–230
probabilistic data association, 204
Electronic intelligence (ELINT)
dual data fusion & resource management (DF&RM) models, 65–66
top-down data fusion automation, 145, 153
Electro-optical (EO) detection, top-down data fusion automation, 153
Ellipsoid intersection queries, multiple-target tracking and, ternary trees, 80–82
Emission spectra, chemical sensor analysis, 746–747
Endsley’s situation awareness model, 450–452
combat identification (CID) mapping, multihypothesis structures, 805–806
data fusion & resource management, dual node architecture, 593–594
Engine test cell correlation, condition-based maintenance, 723–724
Enterprise architecture, requirements derivation, data fusion, 553–556
combat identification (CID) mapping, 779–783
multihypothesis structures, 806
data fusion definitions and, 50
dual data fusion & resource management (DF&RM) models, 64
revised JDL data fusion model, 51–54
situation and threat assessment (STA), 443–445
top-down data fusion automation, puzzle-solving metaphor, 142
Entity tracking, top-down data fusion automation, 154–155
Entropy, combat identification (CID) mapping
Shannon communication analysis, 789–794
Enumeration, hypothesis generation, 601–604
Error boxes, multiple-target tracking and, ternary trees, 80–82
Estimated covariances, covariance intersection algorithm, 337–338
data fusion & resource management, dual node network (DNN) architecture, 568–575
information fusion design, user interaction with, 518–519
Event sensing, information fusion design, 513
Evidence combination, top-down data fusion automation, 143
Evidence constraints, situation and threat assessment, 447–448
Expectation-based analysis, top-down data fusion automation, 162
Expected a posteriori (EAP) estimators
Bayesian state estimation, 374
finite-set statistics, 372–374
Expert systems (ES), condition-based maintenance, 718–719
health assessment example, 726–727
Explicit data representation, database management systems, 631–634
Explicit entities, track coincidence, 156
minimum mean squared error estimation, 347–348
uncertainty transformation, 349
particle filtering, 178
Chapman-Komologorov equation, 180–182
unscented filter, linearization errors, 358–361
unscented transformation, 345–346
Extended operations, data-base management systems, Boolean logic and, 650–652
Failure analysis, condition-based maintenance, industrial gearbox example, 711–712
chemical and biological sensors
performance evaluation, 757–758
flexible-window maximum likelihood-probabilistic data association estimator, 233–240
interacting multiple model-probabilistic data association filter (IMMPDAF), track formation, 222–223
multitarget likelihood functions, 395–397
Faraday plate, ion mobility spectrometer (IMS), chemical sensor analysis, 744
Fatigue particle concentrations, condition-based maintenance, industrial gearbox example, 715–719
Fault condition, condition-based maintenance, data fusion construct, 723
Fault-tolerant distributed data fusion, covariance intersection, distributed data fusion, 339
Feasible hypotheses, identification of, 604
Feature-class query, object-oriented data base management systems, 635–637
automatic target recognition (ATR), pixel-level fusion, 96
data fusion construct, 722
industrial gearbox example, 713–714
automatic target recognition, 96–98
condition-based maintenance, 705
health assessment example, 726–727
industrial gearbox example, 716–719
multisensor data fusion, 8
Features in/features out (FEI/FEO), revised JDL data fusion signal/feature assessment, 53
Feature subspace classification, condition-based maintenance, industrial gearbox example, 716–719
Feature vectors, multisensor data fusion, 7–8
Feedback, network-centric distributed data fusion, 418–420
performance evaluation, 429–433
Feynman diagrams, human-computer interaction, 543
situation and threat assessment, human response models, 469–470
situation and threat assessment models, 455–457
Field-effect transistor (FET), surface acoustic wave (SAW) analysis, 745
Fine gating, multiple-frame assignments, 309–310
future research issues, 406
generalized measurements, 382–384
multisource-multitarget calculus, 390–394
belief-mass functions, 391–392
density functions and set integrals, 391
functional and set derivatives, 392–393
probability generating functionals, 392
random finite sets, 391
Fister Inconsistency-B (FI-B), combat identification (CID) mapping, 793–794
Fister-Mitchel Dempster–Shafer fusion, combat identification (CID) mapping, 800
Fitness function, data registration meta-heuristics, 123–124
Flame photometric detection (FPD), chemical sensor analysis, 745
Flexibility, decentralized data fusion, 320
Flexible-window maximum likelihood-probabilistic data association estimator, low observable target tracking, 230–240
Floating point operation (FLOP), interacting multiple model-probabilistic data association filter (IMMPDAF), 230
Flow-down process, requirements derivation, data fusion, 551–553
Fluid systems, condition-based maintenance, 719–727
data fusion construct, 722–723
engine test cell correlation, 723
health assessment example, 762–727
lubrication system function, 719–722
operational data, metasensor processing, 724–726
turbine engine lubrication system simulation, 722
Fluorescence spectra, chemical sensor analysis, 746–747
Fokker-Planck equation (FPE), finite-set statistics, 372–374
Force Battle Command Brigade-and-Below (FBCB2), Army Battle Command System, 763–764
Forward-looking infrared (FLIR) imaging sensor
automatic target recognition (ATR)
pixel-level fusion, 96
image and spatial data fusion, 92
top-down data fusion automation, 145
Fourier transform infrared (FTIR) spectroscopy, chemical sensor analysis, 746–747
f-refinement taxonomy, combat identification (CID) mapping, 803–805
Friendly replies uncorrelated-in-time (FRUIT), combat identification (CID), 795–797
Functional derivatives, finite-set statistics, 392–393
Fundamental bicycle model, unscented filter, high-order nonlinear systems, 362–363
Fused situation awareness product, top-down data fusion automation, puzzle-solving metaphor, 139–142
Fusion, defined, 16
combat identification (CID), 797–802
Bayesian and orthodox Dempster–Shafer results, 801–802
modified Dempster–Shafer approach, 799–800
top-down data fusion automation, 152–153
Fusion rules, distributed decision fusion, optimization, 171–175
automatic target recognition (ATR), pixel-level fusion, 96
chemical and biological sensors, inferencing protocols, 751–757
condition-based maintenance, 718–719
contextual knowledge, 158
data-base management systems, path-planning applications, 645–647
situation and threat assessment models, 456–457
vague measurements, random set uncertainty models, 384–385
Fuzzy reasoning, data-base management systems, Boolean logic and, 647–652
multiple-target tracking, 75–77
multiple-target tracking research, search radius increases, 85–86
particle filtering, Monte Carlo integration, 182–183
probabilistic data association filter (PDAF), measurement validation, 206
Generalized Belief Propagation formula, situation and threat assessment, 488–489
Geodesy applications, spatial data fusion, 103–107
Geographic information system (GIS)
historical overview, 621
image and spatial data fusion, 91–92
Geometric tests, multiple-target tracking, gating criteria, 76–77
Geospatial intelligence (GEOINT), spatial data fusion, 108–110
Geospatial preparation of the environment (GPE), geospatial intelligence spatial data fusion, 108–110
Global Command and Control System-Army (GCCS-A), Army Battle Command System, 762–763
Gradient maxima, data registration, 119–122
Graph matching, situation and threat assessment, state estimation, 484–485
Ground and satellite data fusion, Army Battle Command System (ABCS)
common operating picture, 761
disaster management applications, 766–770
final recommendations and future research issues, 770
Force Battle Command Brigade-and-Below, 763–764
Global Command and Control System-Army, 762–763
glossary of terms, 771
ground sensors, 766
information fusion and decision making, 761
joint command and control, 761
remote sensing, ground-based systems, 764
situational awareness, 760–761
tactical unmanned aerial vehicles and aerostats, 764–765
Ground-based imaging/nonimaging systems, Army Battle Command System, 764–766
Ground sensors, Army Battle Command System, 766
Gruences, data registration meta-heuristics, 122–124
Haptic interfaces, human-computer interaction, 543
Hausdorff distance, multitarget miss distance, 402
Health assessment, condition-based maintenance
automated reasoning shell tool, 726–727
data fusion construct, 723
Heisenberg uncertainty principle, information fusion design, needs prioritization, 508
Heterogeneous sensor fusion, chemical and biological sensors, 751–757
decentralized data fusion, 323
finite-set statistics, 371
data-base management systems, path-planning applications, 642–647
multisensor data fusion, performance evaluation, 669–673
network-centric distributed data fusion, 418–420
performance evaluation, 429–433
Hierarchical reasoning, database management systems, 622–625
Higher-level data fusion, Salerno’s higher-level fusion model, 452–453
Higher-order terms (HOT), particle filtering, Chapman-Komologorov equation, 180–182
High frequency direction finding (HFDF) systems, data fusion and, 19–21
High-order nonlinear systems, unscented filter, 361–363
High-resolution spatial representation, object-oriented data base management systems, 636–637
Hilbert curve, elastic transformations, data registration, 129–130
Hill climbing algorithm, data registration, 120
Hostile environment, net-centric data fusion systems in, 37
abnormal cognition and perception, 544
future research issues, 543–546
haptic interfaces, 543
information fusion design, 513–514
multisensory experiments, 545–546
quantitative experiments, 544–545
sonification, 542
technological evolution in, 539–543
three-dimensional visualization, 539–541
data fusion history and, 18
information processing cycle and, 32–33
situation and threat assessment, 475–480
top-down data fusion automation, 153
all-source, 154
JDL fusion model, 159
strengths and limitations, 160
Human response models, situation and threat assessment, 469–470
Human silhouettes images, data registration, 119–122
Hybrid spatial feature representation, object-oriented data base management systems, 637
Hydro-FACT software, multisensor data fusion, 683–687
data fusion & resource management, dual node architecture, 587–589
defined, 600
input data characteristics, 604–605
output data characteristics, 606
problem space characterization, 604–606
problem space to solution space mapping, 606–607
situation and threat assessment, 482
Hypothesis formation, wavelet-based data registration, range images, 126–127
defined, 600
feasible hypotheses identification, 604
problem space to solution space mapping, 604
data fusion & resource management, dual node architecture, 587–589
defined, 600
assignment problem, 609
deterministic approaches, 612–615
technique comparisons, 609–611
two-dimensional vs. N-dimensional performance, 611
situation and threat assessment, 482
Hypothesis structure, situation and threat assessment, 493–494
Ideal receiver, target declaration, 296
combat identification (CID) mapping
Shannon communication system, 785–788
image and spatial data fusion, 94
predictive diagnostics, model-based identification and damage estimation, 708–709
Identification friend-or-foe (IFF), combat identification (CID) mapping, 776–778
Shannon communication analysis, 785–788
Identification information measurement, combat identification (CID) mapping, 791–794
future research issues, 110–111
geospatial intelligence, 108–110
multisensor automatic target recognition, 95–100
spatial data applications, 102–107
ImageLock Data Fusion software, multisensor data fusion, 683–687
Army Battle Command System, 764–766
image data fusion enhancement, 100
situation and threat assessment, 475–480
IMM-MHT algorithm, adaptive maximum likelihood-probabilistic data association estimator, 240
combat identification (CID) mapping, 783–784
multihypothesis structures, 806
data fusion & resource management, dual node architectures, 585
dual data fusion & resource management (DF&RM) models, 65
functional data fusion model, 25
revised JDL data fusion model, 51–52, 55
threat assessment, 442
Impedance data, condition-based maintenance, electrochemical systems, 729–730
Implementation efficiency, database management system design, 629
Implicit entities, track coincidence, 156
Importance sampling, particle filtering, Monte Carlo integration, 182–183
Improvised explosive device (IED), top-down data fusion automation, 156–157
Independence theorem, multiple-hypothesis tracking, independent scan association functions, 286
Independent multiple-hypothesis tracking, 285–288
Independent target/component dynamics, situation and threat assessment, 446
Indexing cells, data-base management systems, Boolean logic and, 648–652
Industrial gearbox, condition-based maintenance, 710–719
Industrial robotics, image and spatial data fusion, 92
Infeasible condition, top-down data fusion automation, 152–153
Inference, situation and threat assessment (STA), 442–446
relationships and entity states, 443–445
Inference model, situation and threat assessment, 470
Inferencing networks, chemical and biological sensors, heterogeneous sensor fusion, 751–757
Infon (Devlin’s concept of), situation and threat assessment, 453–455
Informal philosophy, multisensor data fusion performance evaluation, 659–661
defined, 19
Information domain, combat identification (CID) fusion methodologies, 797–798
Information exploitation and response, dual data fusion & resource management (DF&RM) models, 63–65
Information extraction fusion model, top-down data fusion automation, 146–152
combat identification (CID) mapping, entity assessment, 779–783
revised JDL data fusion model, 56–58
Information fragments, top-down data fusion automation, puzzle-solving metaphor, 139–142
Information fusion engine for real-time decision-making (INFERD), situation and threat assessment, state estimation, 485
Army Battle Command System, 761
Bayesian and orthodox Dempster–Shafer results, 801–802
canonical mappings, 804
choice, uncertainty, and entropy, 789–794
Dempster–Shafer approach, 799–801
future research issues, 809–811
identification system principles, 785–788
identification vector formation, 788–789
IFF sensor uncertainties, 794–797
information properties and methodologies, 797–799
multihypothesis structures, 778–784
multihypothesis taxonomies, 802–805
response mapping, 805
combat identification (CID) mapping, 800–801
contextual information, 508–509
data fusion information group model, 510–514
Information graph, distributed data fusion, network-centric architecture, 417–421
Information matrix fusion, network-centric distributed data fusion, 426
resource management and, 30
TRIP resource management and data fusion model, 39
Information overload, multisensor data fusion, 12
combat identification (CID), 775–776
Information-processing cycle (IPC)
data fusion in, 28
defined, 23
TRIP resource management and data fusion model, 39–43
Information requirements, top-down data fusion automation, 143–144
Information sources, multisensor data fusion, 13
Information theory, combat identification (CID) mapping, 784–794
choice, uncertainty and entropy, 789–794
identification system, 785–788
identification vector formation, 788–789
automatic target recognition (ATR), decision-level fusion, 98–99
chemical sensor analysis, 746–747
Initialization states, multisource-multitarget Bayes filters, 400–401
Input data characteristics, hypothesis evaluation, 604–605
Input/output (I/O), Dasarathy’s input/output model, 59–61
Integrated Definition 5 (IDEF5), situation and threat assessment, 463
Integrated spatial/nonspatial data representation, object-oriented data base management systems, 637–638
Integration, geospatial data fusion, 103
Army Battle Command System disaster management assessment, 770
data fusion history and, 17–18
Interacting multiple model-probabilistic data association filter (IMMPDAF)
maneuvering target tracking, 220–230
track termination, 229
overview, 204
Interactions, information fusion design, user control, 517–518
Interface actions, information fusion design, 516–517
Intuitive algorithm development, database management system design, 626
Ion mobility spectrometer (IMS), chemical sensor analysis, 744
nonlinear data fusion, minimum mean squared error estimation, 347–348
particle filtering, Chapman-Komologorov equation, 180–182
Joint assignment matrix (JAM), combinatorics, 244
computational efficiency, 250–251
crude permanent approximations, 252–253
multiple-hypothesis tracking, 248–249
Joint command and control, Army Battle Command System, 761–762
Joint Directors of Laboratories (JDL) Data Fusion Working Group
combat identification (CID) mapping, 776–778
level 2 structures, 783
multihypothesis structures, 802–805
situational awareness and expansion, 805–806
tactical element recognition, 806–809
data fusion 1998 model revision, 47–49
Dasarathy’s input/output model, 59–61
information flow, cross-levels, 56–58
level 0, signal/feature assessment, 52–53
level 1, entity assessment, 53–54
level 2, situation assessment, 54
level 3, impact assessment, 55
level 4, process assessment, 55–56
level 5, user refinement, 58–59
recommended definition refinements, 51–52
resource management and, 61–65
resource management processing levels, 65–66
situation and threat assessment, 448–453
data fusion & resource management, architectures, 563–565
distributed data fusion model, 412–413
functional data fusion model, 23–26
information fusion design, 510–514
multisensor data fusion process model, 8–9
situation and threat assessment, definitions, 438–442
top-down data fusion automation, HUMINT and, 159
Joint land attack cruise missile defense elevated network sensor (JLENS), Army Battle Command System, 765–766
Joint multitarget estimator (JoME), basic principles, 402–403
Joint Oil Analysis Program (JOAP), condition-based maintenance, industrial gearbox example, 714–719
Joint posteriors, multiple-hypothesis tracking, independent scan association functions, 286–287
Joint probabilistic data algorithm (JPDA)
multiple-frame assignments, 300
Joint Service Image Processing System (JSIPS), image and spatial data fusion, 91
Jurkat-Ryser bound, combinatoric approximations, permanent inequalities, 254–255
covariance intersection algorithm, 324–327
distributed data fusion, 329–333
known-independent information, 333–336
data fusion history automation and, 19–21
data fusion & resource management, dual node architecture, 570–575
interacting multiple model-probabilistic data association filter (IMMPDAF), 226–227
multiple-target tracking, 72
multisensor data fusion, 8, 10–11
nonlinear data fusion, 346
minimum mean squared error estimation, 346–348
Chapman-Komologorov equation, 180–182
single-target tracking, Bayesian formulation, 271–274
high-order nonlinear systems, 361–363
kd-trees, multiple-target tracking
Key locations, top-down data fusion automation, 156
Khoros software, multisensor data fusion, 679–687
Kinematics-based trackers, top-down data fusion automation, 155
Kivacich fusion taxonomy, data fusion & resource management, dual node architecture, 592–593
Knowledge, defined, 19
Knowledge-based information fusion (KBIF) strategies, information fusion design, 525–526
Knowledge-based systems, multisensor data fusion, 11
KnowledgeBoard software, multisensor data fusion, 683–687
Known-independent information, covariance intersection, decentralized data fusion, 333–336
Lagrangian relaxation algorithm, multiple-frame assignments, 311–313
Laplacian equations, combinatorics, 249–250
Large-scale data association, combinatorics, 258–261
Layered box tree, multiple-target tracking, 83–84, 86–87
Learning belief networks, situation and threat assessment, 489
Level 0. See Signal/feature assessment
Level 1. See Entity assessment
Level 2. See Situation assessment
Level 3. See Impact assessment
Level 4. See Process assessment
Level 5. See User refinement
Levenberg-Marquadt optimization
condition-based maintenance, state-of-charge (SOC) analysis, 733
Light amplification for detection and ranging (LADAR)
automatic target recognition (ATR)
pixel-level fusion, 96
image and spatial data fusion, 92
Light scattering, chemical sensor analysis, 746–747
ambiguously generated ambiguous (AGA) measurements, 388–389
Bayesian formulation, 267
sensor models, 379
independent scan association functions, 285–286
scan and association likelihood functions, 280–281
multiple-target tracking, Bayesian formula, 275–276
multitarget functions, 395–397
particle filtering resampling, 186–187
single-target tracking, Bayesian formulation, 269–274
unambiguously generated ambiguous (UGA) measurements, 385–386
Likelihood ratio detection and tracking, 289–296
basic definitions and relations, 290–291
log-likelihood ratios, 294–295
measurement likelihood ratio, 291–292
Neyman-Pearson declaration criterion, 296
target present declaration, 295–296
track-before-detect, 296
Linearization errors, unscented filter, 358–361
Linear minimum mean squared error (LMMSE), network-centric distributed data fusion, 427
Line-of-bearing, single-target tracking, Bayesian formulation, 272–274
Link analysis methods, situation and threat assessment, state estimation, 483–484
Local analysis, top-down data fusion automation, 160
Local decision makers, distributed decision fusion, 169–170
Lockheed Martin data fusion workstation, multisensor data fusion software, 679–687
Logic, situation and threat assessment, 453–457
detection and tracking, 294–295
multiple-target tracking, 75–77
adaptive maximum likelihood-probabilistic data association estimator, 237–240
flexible-window maximum likelihood-probabilistic data association estimator, 231
Low observable target tracking, flexible-window maximum likelihood-probabilistic data association estimator, 230–240
Low-resolution spatial representation, object-oriented data base management systems, 636
Lubrication systems, condition-based maintenance
turbine engine simulation model and metasensors, 722
Machine-user interaction performance, information fusion design, 530–532
Macro-level fusion design, information fusion, 505–507
multiple-target tracking, 75–77
network-centric distributed data fusion, naïve fusion, 424–425
Majority logic, distributed decision fusion, 172–175
Maneuver detection model, interacting multiple model-probabilistic data association filter (IMMPDAF), 228–229
Maneuver model, interacting multiple model-probabilistic data association filter (IMMPDAF), 228
multimodal image registration, 130–132
Marginal multitarget estimator (MaME), basic principles, 402–403
Marginal posteriors, multiple-hypothesis tracking, independent scan association functions, 286–287
Markov chain Monte Carlo, particle filtering, 187–188
Markov densities, multitarget motion models, 397–399
likelihood ratio detection and tracking, 290–291
multiple-target tracking, Bayesian formula, 275
Markov time-prediction integral, finite-set statistics, 372–374
Bayesian recursive filtering, 378–379
Mathcad software, multisensor data fusion, 679–687
condition-based maintenance, electrochemical systems, 729–730
multisensor data fusion technology, 10–11
Mathematica software, multisensor data fusion, 679–687
covariance intersection, 341–342
multisensor data fusion, 679–687
Maximum a posteriori (MAP) fusion
hypothesis evaluation problem space to solution space mapping, 606–607
network-centric distributed data fusion, 426–427
Maximum likelihood estimation (MLE). See also Flexible-window maximum likelihood-probabilistic data association estimator
hypothesis evaluation problem space to solution space mapping, 606–607
multitarget state estimators, 402–403
probabilistic data association, 204
target motion analysis, 210–220
covariance intersection algorithm, 337–338
unscented filter, linearization errors, 358–361
Measurement and signature intelligence (MASINT)
biological fusion metaphor, 139
situation and threat assessment, 475–480
Measurement likelihood ratio, detection and tracking, 291–292
Measurement model, situation and threat assessment, 470
Measurement units, multisource-multitarget Bayes filters, 401
Measurement validation, probabilistic data association filter (PDAF), 206
Measure of correlation (MOC), human-computer interaction, sonification, 542
Measures of effectiveness (MOEs)
multisensor data fusion, 656–657
performance evaluation, 667–673
revised JDL data fusion process assessment, 51, 55–56
Measures of force effectiveness (MOFEs), multisensor data fusion, 656–657
performance evaluation, 667–673
Measures of merit, multisensor data fusion performance evaluation, 667–673
Measures of performance (MOPs)
data fusion & resource management, dual node architectures, 585–587
multisensor data fusion, 656–657
performance evaluation, 667–673
revised JDL data fusion process assessment, 56
Mechanical diagnosis test bed (MDTB), condition-based maintenance, industrial gearbox example, 710–719
Mechanical equipment, multisensor data fusion monitoring of, 6
Mechanical power transmission, condition-based maintenance, 710–719
industrial gearbox example, 710–719
Mechanized observer, data fusion operations and, 19
image and spatial data fusion, 92
multimodal image registration, 130–132
Mental model, user refinement, information fusion design, 510–512
Merged measurements, multiple-target tracking, 277–278
TRIP resource management and data fusion model, 40–43
Meta-heuristics, data registration, 122–124
Metaphysics, data fusion & resource management, dual node architecture, 588
Metasensors, condition-based maintenance
operational data, metasensor processing, 724–726
turbine engine lubrication system, 722–723
information fusion design, performance evaluation, 522
multisensor data fusion performance evaluation, 660–661, 670–673
net-centricity and, 37
Metropolis-Hastings (M-H) process, particle filtering, 189–191
Micro-level fusion design, information fusion, 505–507
multimodal image registration, 130–132
multisensor data fusion performance evaluation, 667–673
revised JDL data fusion impact assessment, 55
automatic target recognition (ATR)
image and spatial data fusion, 92
Minimum mean squared error (MSE), nonlinear estimation, 346–348
Min/max values, multiple-target tracking
binary search and kd-trees, 78–79
Misassigned reports, multiple-target tracking, nearest-neighbor rule limitations, 74
Missed detections, multitarget likelihood functions, 395–397
Mission critical, condition-based maintenance, 704
Mission-driven situational awareness reasoning, combat identification (CID) mapping, 784
Mission objectives, dual data fusion & resource management (DF&RM) models, 65
Mission planning, condition-based maintenance, state-of-charge (SOC) analysis, 733
data-base management systems, path-planning applications, 642–647
ion mobility spectrometer, chemical sensor analysis, 744
Mode-conditioned filtering, interacting multiple model-probabilistic data association filter (IMMPDAF), 227
Mode interaction/mixing, interacting multiple model-probabilistic data association filter (IMMPDAF), 227
condition-based maintenance, batteries, 727–729
predictive diagnostics, 708–709
Model-driven methods, state estimation, situation and threat assessment, 482
Mode update, interacting multiple model-probabilistic data association filter (IMMPDAF), 227
Modularity, net-centricity and, 34–35
Monotonicity, distributed decision fusion, global decision rules, 172–175
covariance intersection, distributed data fusion, 331–332
nonlinear data fusion, unscented transformation, 349–351
target state filtering, 182–183
unscented filter, linearization errors, 358–361
Mosaicking, image and spatial data fusion, 90
Most probable assignment criteria, combinatorics, 246–247
finite-set statistics, 373
Markov densities, 380
Markov densities, multitarget motion models, 397–399
multiple-target tracking, Bayesian formula, 274–275
condition-based maintenance, 702–704
image and spatial data fusion, 90–92
JDL data fusion model revision, 50–51
TRIP resource management and data fusion model, 42–43
association probabilities, 283
conditionally independent scan association likelihood functions, 285–287
conditional target distributions, 283
contacts, 279
data association hypothesis, 279–280
data association likelihood function, 282
independent recursion, 287–288
scan association likelihood function, 280–281
combat identification (CID) mapping, 778–784
situational awareness and expansion, 805–806
tactical element recognition, 806–809
data association algorithms, 300–301
data fusion & resource management, dual node architectures, 584
multiple-target tracking and, 74–75
multisensor data fusion, 10–11
probabilistic data association, 204
unified recursion, 277
Multi-INT data categories, multisensor data fusion survey, 694–699
Multimodal imagery, data registration, 130–132
Multiobject density functions, finite-set statistics, 391
Multiple-frame assignments, data association algorithms
assignment formulation, 302–306
complexity, 314
future research issues, 314–316
improvements, 314
Lagrangian relaxation, 311–313
net-centric assignments, 316
sliding window track maintenance, 307–308, 315
automatic target recognition, 99–100
sensor fusion, unscented transformation, 363–365
Multiple model filtering, covariance intersection, distributed data fusion, 339
Multiple object coincidence, top-down data fusion automation, 153
Multiple sensor tracking, particle filter samples, 191–199
Multiple target interactions, situation and threat assessment, 446
definitions, 267
covariance intersection, track-to-track data fusion, 339
binary search and kd-trees, 77–79
future research issues, 87
track splitting and multiple hypotheses, 74–75
data-base management systems, path-planning applications, 643–647
image data fusion enhancement, 100
wavelet-based data registration, range images, 125–127
Multisensor data fusion, registration issues 116–117
taxonomy, 678
evolving technology for, 10–11
human-computer interaction, 545–546
information sources for, 13
multitarget likelihood functions, 396–397
contextual analysis, 658
military effectiveness measures of merit, 667–673
testing and evaluation criteria, 659–661
testing and evaluation philosophies, 658–659
predictive diagnostics, toolkit for, 709
capabilities assessment, 692–699
Multisensor localizer (MSL), sound surveillance system and, 20–21
Multisensor tracker (MST), sound surveillance system and, 20–21
Multisource integration (MSI), combat identification (CID), identification friend-or-foe (IFF) uncertainties, 794–797
Multitarget miss distance, basic principles, 402–403
Multitarget posterior distributions, Bayesian formula, finite-set statistics, 376–377
Mutual information, multimodal image registration, 130–132
Naïve fusion, network-centric distributed data fusion, 424–425
National Geospatial Intelligence Agency (NGA), image and spatial data fusion, 91–92
Natural language processing, data-base management systems, Boolean logic and, 647–652
Natural selection, biological fusion metaphor, 138–139
N-dimensional assignment algorithms
multiple-frame assignments, 313
multiple-frame assignments, 300
multiple-target tracking, 72–74
Needs prioritization, information fusion design, 507–508
Net-Centric Enterprise Solutions for Interoperability (NESI), requirements derivation, data fusion, 559
data fusion & resource management, dual node network (DNN) architecture, 566–568
centralized architecture, 417–418
distributed architecture, 420–421
feedback hierarchical architecture, 419–420
non-feedback hierarchical architecture, 418–419
multiple-frame assignments, 316
requirements derivation, data fusion, 557–559
Network-centric architectures, distributed data fusion
communications, 416
covariance intersection fusion, 427–429
cross-covariance fusion, 425
distributed information, 421–424
fusion algorithm, 416–417, 421–429
future research issues, 433
information matrix fusion, 426
overview and basic principles, 411–417
performance evaluation, 429–433
Network-centric warfare (NCW), decentralized data fusion, 319–320
Networked operations, multisensor data fusion, 12
Network flow analysis, hypothesis selection, 609
Neural networks, condition-based maintenance, 718–719
health assessment example, 726–727
state-of-charge (SOC) analysis, 733
Neyman-Pearson declaration criteria, 296
Node processing, data fusion & resource management, 575–577
Noise models, data registration meta-heuristics, 123–124
Nonbenign urn model, combat identification (CID) mapping, 798–799
non-Canonical mapping, combat identification (CID), 803–805
Noncooperative target recognition (NCTR), combat identification (CID) mapping
identification vector, 788–789
Shannon communication analysis, 786–788
Nondestructive examination (NDE) software, multisensor data fusion, 683–687
Nonlinear ranges, chemical and biological sensors, quantitative/qualitative analysis, 750–751
covariance intersection, 339–340
future research issues, 366
multilevel sensor fusion, 363–365
uncertainty transformation, 348–349
high-order nonlinear system, 361–363
unscented transformation, 349–352
discontinuous transformation, 354–356
polar-to-Cartesian coordinates, 353–354
properties, 352
sigma points, 351
Nonparametric probabilistic data association, 210
Nontraditional data, Bayesian filtering, 380–382
Normalized run time, particle filtering, 194–199
NOT rule, chemical and biological sensors, concentration consistency, 756–757
Nuclear biological chemical (NBC) detectors, Army Battle Command System, 765–766
Objectivity/DB software, multisensor data fusion, 683–687
Object-oriented data assessment (OODA)
data fusion & resource management, dual node architecture, 591–594
information fusion design, 516–517
Object-oriented data base management system (OODBMS)
situation and threat assessment, 495–496
spatial representation, 634–637
Object query language (OQL), situation and threat assessment, 462
combat identification (CID) mapping, 776–777
functional data fusion model, 25
revised JDL data fusion model, 51–54
situation and threat assessment, 440
Object-relational database model, historical overview, 621–622
data fusion, condition-based maintenance, 722–723
resource management and information processing, 30
situation and threat assessment, human response models, 469–470
TRIP resource management and data fusion model, 39
Observation-driven situational awareness, combat identification (CID) mapping, 784
Observe, orient, decide, act (OODA) model, 61
Observed image, data registration, 116–117
Ocean surveillance systems, multisensor data fusion, 3–5
Oil debris analysis, condition-based maintenance, industrial gearbox example, 712
data fusion & resource management, dual node architecture, 588
information fusion design, needs prioritization, 507–508
situation and threat assessment, 460–466
core situation awareness ontology, 463–464
semantic registration, 474–475
specification languages, 462–463
threat and vulnerability, 464–466
Open communications network, situation and threat assessment, 475–480
Open geospatial consortium (OGC), Army Battle Command System disaster management assessment, 768–770
multisensor data fusion survey, 692–699
requirements derivation, data fusion, 555
Operational data, condition-based maintenance, metasensor processing, 724–726
data fusion automation and, 21
TRIP resource management and data fusion model, 39–43
Operational Intelligence Center (OIC), data fusion history and, 17–18
Opportunity, situation and threat assessment models, 467–469
Optimal assignment method, combinatorics, 246–247
Optimal control, data fusion & resource management, dual node architecture, 568–575
Optimal estimator, data fusion & resource management, dual node architecture, 568–575
Bayesian formula, 374
finite-set statistics, 375–377
data registration meta-heuristics, 123–124
Order of n2(O(n2)) performance
combinatorics, computational efficiency, 250–251
binary search and kd-trees, 77–79
multiple-target tracking research, 70
unscented transform, nonlinear data fusion, 352
Organizational philosophy, multisensor data fusion performance evaluation, 659–661
Organization discovery, top-down data fusion automation, all-source, 154
chemical and biological sensors, concentration consistency, 756–757
distributed decision fusion, global decision rules, 173–175
Output data characteristics, hypothesis evaluation, 606
Output file creation, three-dimensional imagery enhancement, 101
Output product creation, three-dimensional imagery enhancement, 101
Overhead efficiency, database management system design, 628
Paradigm selection, top-down data fusion automation, 148–152
Parallel fusion network, distributed decision fusion, 168–171
Parametric probabilistic data association, 210
condition-based maintenance, industrial gearbox example, 715–719
future research issues, 199–200
Markov chain Monte Carlo, 187–188
performance evaluations, 191–199
target state filtering problem, 178–183
Chapman-Komolgorov equation, 179–182
Monte Carlo integration and importance sampling, 182–183
Particle impoverishment, update function, 187
Pathogen detection, chemical and biological sensors, complexity analysis, 741–743
Path-planning applications, data-base management systems, 639–647
Pattern recognition techniques
chemical and biological sensors, inferencing protocols, 751–751
multisensor data fusion, 10–11
multisensor data fusion, 12
TRIP resource management and data fusion model, 40–43
Penn State ARL enveloping technique, condition-based maintenance
industrial gearbox example, 715–719
operational data, metasensor processing, 724–726
Penn State ARL mechanical diagnostics test bed, condition-based maintenance, 711–712
Endsley’s situation awareness model, 450
human-computer interaction, 544
database management system design, 627–629
user refinement, information fusion design, 520–529
display/interface design, 527–529
dynamic decision making, 523–526
metrics, 522
situational awareness, 520–522
work analysis/task analysis, 526–527
dual data fusion & resource management (DF&RM) models, 66
network-centric distributed data fusion, 429–433
Permanent inequalities, combinatoric approximations, 253–254
Phenomenology, data fusion & resource management, dual node architecture, 588
Photoionization detection (PID), chemical sensor analysis, 745–746
Physical models, hypothesis enumeration, 601–604
Pixel-level fusion, automatic target recognition (ATR), 95–96
Plasmagram, ion mobility spectrometer, chemical sensor analysis, 744
Poisson model, probabilistic data association filter (PDAF), 209–210
Polar to Cartesian coordinates, unscented transformation, nonlinear data fusion, 353–354
Polya urn model, combat identification (CID) mapping, 798
Polymerase chain reaction (PCR), biological sensors, 749
Position reports, multiple-target tracking and, 71–72
Posterior, multiple-target tracking, Bayesian formula, 276–277
Bayesian formulation, 267
single-target tracking, Bayesian formulation, 270–274
Precision, situation and threat assessment models, 455–457
Prediction density, particle filtering resampling, 186–187
Prediction equation, probabilistic data association filter (PDAF), 208
electrochemical systems, 727–734
ARMA state-of-charge prediction, 732
neural networks state-of-charge prediction, 733–734
sensor/virtual sensor data fusion, 730–734
condition-based maintenance systems, 702–704
decision-level fusion, 706–708
feature-level fusion, 705
mechanical power transmission, 710–719
model-based development, 708–709
multisensor fusion toolkit, 709
overview, 702
future research issues, 734
Predictive situational awareness (PSA), combat identification (CID) mapping, 806–809
Preprocessing/subobject refinement
functional data fusion model, 24
multiple-frame assignments, algorithms, 309–311
Preventive mode, information fusion design, cognitive processing, dynamic decision making, 524–526
Prior distribution, Bayesian formulation, 267
Prioritization of needs, information fusion design, 507–508
Priority kd-trees, multiple-target tracking, 82–84
Priority queue, multiple-target tracking research, 86–87
Proactive mode, information fusion design, cognitive processing, dynamic decision making, 524–526
Probabilistic data association filter (PDAF), 205–210
association probabilities, 208–209
data fusion & resource management, dual node architectures, 584
flexible-window ML-PDA estimator, low observable targets, 230–240
computational load, 240
estimator formulation, 231–232
maximum likelihood-probabilistic data association estimator, 234–235
interacting multiple model-probabilistic data association filter, maneuvering target tracking, 220–230
benign motion model, 228
maneuver detection model, 228–229
maneuver model, 228
mode-conditioned filtering, 227
mode interaction/mixing, 227
mode update, 227
probabilistic data association, 224–226
state combination, 228
track termination, 229
low observable target motion analysis, 210–220
amplitude information feature, 210–212
Cramér-Rao lower bound, 216–217
maximum likelihood estimator-probabilistic data algorithm, 214–216
measurement validation, 206
nonparametric PDA, 210
parametric PDA, 210
future research issues, 199–200
Markov chain Monte Carlo, 187–188
performance evaluations, 191–199
target state filtering problem, 178–183
Chapman-Komolgorov equation, 179–182
Monte Carlo integration and importance sampling, 182–183
prediction equations, 208
state and covariance update, 207
hypothesis enumeration, 603
situation and threat assessment, 456–457
Probability density function (PDF)
flexible-window maximum likelihood-probabilistic data association estimator, 233–240
Probability generating functionals, finite-set statistics, 392
Probability high-density (PHD) filters, multisource-multitarget information fusion, 403–406
Probability hypothesis density, multisource-multitarget information fusion, 404–406
Probability information content (PIC), combat identification (CID) mapping, choice, entropy, and uncertainty, 790–794
Probability mass function (PMF), probabilistic data association filter (PDAF), 209
Probability-mass function, sensor model likelihood functions, 379
Probability of association, multiple-target tracking, gating criteria, 75–77
condition-based maintenance, 704–706
data-base management systems, path-planning applications, 640–647
data fusion & resource management, 581–584
multiple-frame assignments, 310–311
top-down data fusion automation, 144–145
artificial neural systems, 161
puzzle-solving metaphor, 141–142
Problem space to solution space mapping
hypothesis evaluation, 604–607
hypothesis generation, 604
dual data fusion & resource management (DF&RM) models, 62–65
Process-level models, top-down data fusion automation, 145–152
data fusion & resource management
dual node architecture, 587–591
dual node architectures, 585–587
dual data fusion & resource management (DF&RM) models, 65
functional data fusion model, 25
revised JDL data fusion model, 51–52, 55–56
Production systems, top-down data fusion automation, 160–161
background and perspectives, 598–599
future research issues, 616
hypothesis evaluation, 604–607
input data characteristics, 604–605
output data characteristics, 606
problem space characterization, 604–606
problem space to solution space mapping, 606–607
hypothesis generation, 601–604
feasible hypotheses identification, 604
assignment problem, 609
deterministic approaches, 612–615
technique comparisons, 609–611
two-dimensional vs. N-dimensional performance, 611
Projection, Endsley’s situation awareness model, 450
Protégé 2000, situation and threat assessment, 462–463
Pseudo-Markov transition, probability high-density filter, 404–405
Pulse position modulation (PPM) equation, combat identification (CID) mapping, identification vector, 789
Puzzle-solving metaphor, top-down data fusion automation, 139–142
data-base management systems, path-planning applications, 642–647
object-oriented data base management systems, 635–637
Quadtree-indexed vector representation, data-base management systems, Boolean logic and, 648–652
Qualitative analysis, chemical and biological sensors
information development, 749–751
information transformation, 754–757
chemical and biological sensors
information development, 749–751
human-computer interaction, 544–545
Radar, multiple-target tracking and history of, 70–72
Radar cross section (RCS), combat identification (CID) mapping
identification vector, 789
Radial basis function (RBF), condition-based maintenance, state-of-charge (SOC) analysis, 733
Radon-Nikodym theorem, finite-set statistics, 394
Raman spectroscopy, chemical sensor analysis, 747
Randles circuit impedance model, condition-based maintenance, electrochemical systems, 729–730
Random set theory, multisource-multitarget information fusion
ambiguously generated ambiguous measurements, 388–389
ambiguously generated unambiguous measurements, 389
Bayes filtering and estimation, 378–380, 400–403
Bayesian models, optimality, computability, 372–374
belief-mass functions, 391–392, 399
classical state estimator limitations, 401
computability, 374
contingent measurement rules, 384–385
Dempster–Shafer evidence, 384
differentiation rules, 394
distributions and measurement units, 401
finite-set statistics, 375–377, 391
formal optimality, 374
future research issues, 406
generalized measurements, 383–384
generalized state-estimates, 389–390
likelihood functions, 379, 395–397
limitations, 375
miss distance, 402
motion models, 373, 380, 398–399
multiobject density functions and set integrals, 391
optimal state estimators, 380, 402
probability generating functionals, 392
probability high-density and cardinalized probability high-density filters, 403–406
robustness, 374
sensor model construction, 379
sensor models, 373
state estimation, 374
unambiguously generated ambiguous measurements, 385–388
unified integration, 403
unified single-target multisource integration, 390
vague fuzzy measurements, 384
Range gate pull off (RGPO), interacting multiple model-probabilistic data association filter (IMMPDAF), 220–230
Range images, wavelet-based data registration, 124–127
combat identification (CID) mapping, 784
condition-based maintenance, health assessment example, 726–727
image and spatial data fusion, 93
top-down data fusion automation, 146–152
puzzle-solving metaphor, 141–142
Receiver operator curves (ROCs)
chemical and biological sensors, quantitative/qualitative analysis, 750–751
information fusion design, user control, 517–518
Recognition model, situation and threat assessment (STA), 443–445
Recognition-primed decision-making (RPD), combat identification (CID) mapping, 809
Recognition primed decision-making (RPD) model, information fusion design, 520–522
likelihood ratio detection and tracking, 292–293
multiple-hypothesis tracking, 284–285
independent recursion, 287–288
single-target tracking, Bayesian formulation, posterior distribution, 270–274
Reference image, data registration, 116–117
Refined registration, defined, 116–117
Registration. See also Data registration
assistance/preprocessing, 127–129
image and spatial data fusion, 90, 92–93
Relational data base management system (RDBMS)
historical overview, 621
situation and threat assessment, 495–496
Relational states, situation and threat assessment, entity state inferences, 444–445
situation and threat assessment, 440
entity state inferences, 443–445
situation assessment and, 54
Reliability, decentralized data fusion, 320
Remaining useful life (RUL), condition-based maintenance, predictive diagnostics, 702–704
Army Battle Command System, 764–766
multimodal image registration, 131–132
multisensor data fusion, 6
Reported crimes data, crime mapping spatial data fusion, 106–107
Representation characteristics, database management systems, spatial representation, 629–634
Requirements derivation in data fusion
comparison of design approaches, 556–557
engineering flow-down approach, 552–553
enterprise architecture approach, 553–556
network-centric architectures, 557–559
requirements analysis process, 550–552
three-dimensional imagery enhancement, 101
Dasarathy’s input/output model, 59–61
data fusion & resource management
dual node architecture, 587–589
information-processing cycle (IPC), 26–30
revised JDL data fusion model, 46
process assessment, 56
Resource relationships, dual data fusion & resource management (DF&RM) models, 64
Resource signal management, dual data fusion & resource management (DF&RM) models, 64
Response mapping, combat identification (CID), 805
Response planning, dual data fusion & resource management (DF&RM) models, 63–65
data fusion & resource management, dual node architecture, 569–575, 587–589
Bayesian formula, target declaration, 295–296
distributed decision fusion, fusion rule optimization, 171–175
single node detection, 167–168
Bayesian formula, 374
finite-set statistics, 375–377
combat identification (CID), 795–797
Root mean square (RMS) tracking error
interacting multiple model-probabilistic data association filter (IMMPDAF), 229–230
Route information, crime mapping spatial data fusion, 106–107
Rumor propagation, decentralized data fusion, 321–323
Ryser’s equation, combinatoric approximations
multiple-hypothesis tracking, 249–250
permanent inequalities, 253–255
Salerno’s higher-level fusion model, situation and threat assessment, 452–453
Satellite imagery. See also Ground and satellite data fusion
image and spatial data fusion, 90–92
Saturation ranges, chemical and biological sensors, quantitative/qualitative analysis, 750–751
Scaling issues, multiple-target tracking research, 85–87
Scan associations, multiple-hypothesis tracking, 279–281
Search efficiency, database management system design, 628
Segment-level fusion, automatic target recognition (ATR), 95–96
Selective identity feature (SIF) octal codes, combat identification (CID), identification friend-or-foe (IFF) uncertainties, 794–797
Self-assessment operations, dual data fusion & resource management (DF&RM) models, 63–65
Self-conflict index (SCI), combat identification (CID) mapping, identification information measurement, 793–794
Self-synchronization, combat identification (CID) mapping, 809
Semantic location translation, contextual knowledge, 158
Semantic Networks Processing System (SNePs), situation and threat assessment, 463
Semantic reasoning, database management systems, 624–625
Semantic registration, situation and threat assessment, 474–475
Semantics, information fusion design, 516–517
Sensor-derived knowledge, top-down data fusion automation, puzzle-solving metaphor, 140–142
Sensor dominance, distributed decision fusion, 172–175
SensorML system, Army Battle Command System disaster management assessment, 768–770
Sensor registration methods, data registration, 117–122
Sensor response model (SRM), chemical and biological sensors, qualitative infor-mation transformation, 755–757
belief-mass functions, multitarget sensor models, 396–397
biological fusion metaphor, 138–139
biological sensors, data fusion adaptation
complexity characteristics, 740–743
concentration consistency, 755–757
future research issues, 757–758
inferencing networks, heterogeneous fusion, 751–753
overview, 739
qualitative information transformation, 754–755
quantitative/qualitative information, 749–751
chemical sensors, data fusion adaptation
colorimetric sensing, 747
complexity characteristics, 740–743
flame photometric detection, 745
ion mobility spectrometer, 744
overview, 739
photoionization detection, 745–746
spectrographic detection, 746–757
surface acoustic wave and electrochemical cells, 745
combat identification (CID) mapping, identification friend-or-foe (IFF) uncertainties, 794–797
condition-based maintenance, 704
defined, 90
finite-set statistics, 373
information fusion design, 512–514
likelihood functions, 379
multimodal image registration, 132
multiple-target tracking, 84–87
single-target tracking, Bayesian formulation, 269
Sensor web enablement (SWE), Army Battle Command System disaster management assessment, 768–770
Sequential Monte Carlo sum (SMS) methods, particle filtering, 178
Service-oriented architectures (SOA)
multisensor data fusion, 12
net-centricity and, 33
Set derivatives, finite-set statistics, 392–393
Set integrals, finite-set statistics, 391
Shannon communication analysis, combat identification (CID) mapping, 784–794
identification process, 785–788
Shortest path problems, hypothesis selection, 609
particle filtering, unscented (transform) Kalman filter, 178
example, 351
dual data fusion & resource management (DF&RM) models, 64
revised JDL data fusion model, 51–53
Similar source integration (SSI), combat identification (CID), 794–797
Simplified recursion, likelihood ratio detection and tracking, 292–293
Army Battle Command System disaster management assessment, 769–770
multisensor data fusion performance evaluation, 662–666
Simultaneous map building, covariance intersection, distributed data fusion, 339
Single integrated air picture (SIAP) system, net-centric multiple-frame assignments, 316
Single node detection, distributed decision fusion, 166–168
Single-pane sliding window, multiple-frame assignments, track maintenance, 307–308
Single sensor tracking, particle filter samples, 191–199
Single-target multisource integration, Bayesian filtering, 390
Single-target tracking, Bayesian formulation, 268–274
information combination, likelihood functions, 273–274
line-of-bearing plus detection likelihood functions, 272–273
posterior distribution, 270
prior information, 269
sensors, 269
Situation and threat assessment (STA)
adaptive situation architectures, 473–474
Army Battle Command System, 760–761
combat identification (CID) mapping, 783
multihypothesis structures, 805–806
confidence normalization, 475–480
semantic registration, 474–475
data fusion & resource management, dual node architectures, 584
deterministic situation logic, 453–455
dual data fusion & resource management (DF&RM) models, 64
Endsley’s model for situation awareness, 450–452
functional data fusion model, 25
future research issues, 496
fuzzy approaches, 456
relationships and entity states, 443–445
information fusion design, 513–514
contextual knowledge, 508
JDL data fusion model, 448–452
logic and situation theory, 453–457
core situation awareness ontology, 463–464
specification languages, 462–463
threat and vulnerability, 464–466
probabilistic approaches, 456–457
revised JDL data fusion model, 51–52, 54
Salerno’s model for higher-level fusion, 452–453
algorithmic techniques, 493
compositional methods, 490–493
state transition data fusion model, 457–459
top-down data fusion automation, 146–152, 151–152
Situation logic, defined, 439
Situation ontology, defined, 439
combat identification (CID) mapping, 777–778
situation and threat assessment, 445
Situation semantics, defined, 439
Situation theory and logic, situation and threat assessment, 453–457
Situation tracking, situation and threat assessment, 446
Skill-rule-knowledge (SRK) model, situation and threat assessment, 470
Sliding window, multiple-frame assignments
future research issues, 315
Smart-meeting applications, data registration, 121–122
Smets pignistic probability, combat identification (CID) mapping, identification information measurement, 792–793
Sonification, human-computer interaction, 542
Sound surveillance system (SOSUS), data fusion automation and, 19–21
Space-time association, top-down data fusion automation, 152–153
Space-time tracking, 153
Sparkle algorithm, image data fusion enhancement, 100
database management systems, 622–625
representation characteristics, 629–634
geospatial intelligence, 108–110
mapping, charting, and geodesy applications, 103–107
database management systems, 631–634
object-oriented data base management systems, 634–637
absence of, 153
situation and threat assessment, 447
Specification languages, situation and threat assessment, 462–463
Specific feature spatial representation, database management systems, 631–634
Spectrographic detection, chemical sensor analysis, 746–747
SPOT satellite, multimodal image registration, 132
Stabilizing noise, covariance intersection, 339–340
Standard data sets, multisensor data fusion performance evaluation, 662–666
Standoff jammer (SOJ), interacting multiple model-probabilistic data association filter (IMMPDAF), 220–230
State combination, interacting multiple model-probabilistic data association filter (IMMPDAF), 228
ambiguously generated ambiguous (AGA) measurements, 389–390
Bayesian formula, 374
optimality, 380
condition-based maintenance, data fusion construct, 722
multisource-multitarget Bayes filters
classical estimation failure, 401
optimality, 402
probabilistic data association filter (PDAF), 206–207
situation and threat assessment, 482–493
algorithmic techniques, 493
compositional methods, 490–493
State-of-charge (SOC) analysis, condition-based maintenance
ARMA prediction, 732
neural networks prediction, 733
sensor/virtual sensor data, 730–734
State sensing, information fusion design, 513
State transition data fusion (STDF) model, situation and threat assessment, 457–459
Static information, geospatial intelligence spatial data fusion, 108–110
Stochastic simulation, situation and threat assessment, 488
Storage efficiency, database management system design, 627–628
Strategic defense initiative (SDI), multiple-target tracking research, 69–70
Subgoal designations, data-base management systems, path-planning applications, 643–647
Sum of squared gradient (SSG) magnitude, multimodal image registration, 131–132
Sum-square error, combat identification (CID), 794–797
Surface acoustic wave (SAW) analysis, chemical sensors, 745
Surveillance systems, multiple-frame assignments
assignment formulation, 302–306
complexity, 314
future research issues, 314–316
improvements, 314
Lagrangian relaxation, 311–313
net-centric assignments, 316
sliding window track maintenance, 307–308, 315
top-down data fusion automation, 144–145
Syntactic models, hypothesis enumeration, 601–604
Synthetic aperture radar (SAR) data
image data fusion enhancement, 100
top-down data fusion automation, 144–145
System and data description, condition-based maintenance, industrial gearbox example, 710–711
Systems architecture, requirements derivation, data fusion, 555
Systems development life cycle (SDLC), Army Battle Command System disaster management assessment, 767–770
data fusion systems implementation
architectural comparisons and applications, 584–591
architectural requirements, 563–580
Bedworth and O’Brien’s omnibus model, 592
Dasarathy fusion model, 591–592
data fusion and resource management systems, 568–580
data fusion node processing, 575–577
dual data association/response planning functions comparisons, 580
dual node network architecture, 565–575
dual resource management/dual node network application, 587–589
impact assessment applications, 585
information processing systems, 563–565
Kovacich fusion taxonomy, 592–593
process assessment applications, 585–587, 589–591
resource management node processing, 577–579
situation awareness applications, 584
unification model, DF&RM/DNN architectures, 591–594
hypothesis selection solutions, 611–615
requirements derivation, data fusion, 551–556
System surveys, multisensor data fusion
capabilities assessment, 692–699
TACFUSION service, requirements derivation, data fusion, 558–559
Tactical element recognition, combat identification (CID) mapping, 806–809
Tactical Unmanned Aerial Vehicles/Aerostates, Army Battle Command System, 764–766
Target declaration, Bayesian formula, 295–296
maximum likelihood-probabilistic data association, 210–220
amplitude information, 210–212
Cramér-Rao lower bound, 216–217
probabilistic data association, 204
Target numbers, Markov densities, multitarget motion models, 397–399
Chapman-Komolgorov equation, 179–182
Monte Carlo integration and importance sampling, 182–183
Target state space, single-target tracking, Bayesian formulation, 268–269
Target track density, particle filtering, 184–185
flexible-window ML-PDA estimator, low observable targets, 230–240
computational load, 240
estimator formulation, 231–232
maximum likelihood-probabilistic data association estimator, 234–235
interacting multiple model-probabilistic data association filter, maneuvering target tracking, 220–230
benign motion model, 228
maneuver detection model, 228–229
maneuver model, 228
mode-conditioned filtering, 227
mode interaction/mixing, 227
mode update, 227
probabilistic data association, 224–226
state combination, 228
track termination, 229
low observable target motion analysis, 210–220
amplitude information feature, 210–212
Cramér-Rao lower bound, 216–217
maximum likelihood estimator-probabilistic data algorithm, 214–216
future research issues, 199–200
Markov chain Monte Carlo, 187–188
performance evaluations, 191–199
target state filtering problem, 178–183
Chapman-Komolgorov equation, 179–182
Monte Carlo integration and importance sampling, 182–183
probabilistic data, overview, 204–205
probabilistic data algorithm filter, 205–210
association probabilities, 208–209
measurement validation, 206
nonparametric PDA, 210
parametric PDA, 210
prediction equations, 208
state and covariance update, 207
multisensor data fusion, 10–11
top-down data fusion automation, 149–152
data fusion & resource management, resource node processing, 577–579
information fusion design, 526–527
TRIP resource management and data fusion model, 39–40
Taxonomic refinement series, combat identification (CID) mapping, 803–805
Taxonomic structure, combat identification (CID) mapping, 776–778
JDL level 2 structures, 783
JDL level 3 structures, 783–784
multihypothesis structures, 802–805
Taylor series, nonlinear data fusion, uncertainty transformation, 348–349
Technical architecture, requirements derivation, data fusion, 555
Template methods, situation and threat assessment, state estimation, 485–486
Temporal reasoning, database management systems, 622–625
Temporal registration, data registration, 121–122
Ternary trees, multiple-target tracking, 79–82
industrial gearbox example, 710–711
lubrication system test bench, 721–722
multisensor data fusion performance evaluation, 662–666
Testing and evaluation (T&E) protocols, multisensor data fusion, 657–661
Threat assessment. See also Situation and threat assessment (STA)
combat identification (CID) mapping, 778
Threat model, situation and threat assessment, 467–469
Three-dimensional imagery, image data fusion enhancement, 101
Three-dimensional mensuration-estimation, image and spatial data fusion, 90
Three-dimensional visualization techniques, human-computer interaction, 539–541
data-base management systems, path-planning applications, 640–647
all-source model, 154
biological fusion metaphor, 138–139
biologically motivated process model, 145–152
command and control metaphor, 142–143
commensurate/noncommensurate data, 145
context support extensions, 159–160
evidence combination, 143
information requirements, 143–144
JDL fusion model, HUMINT applications, 159
key locations, 156
problem dimensionality, 144–145
process model extensions, 152–153
puzzle-solving metaphor, 139–142
track coincidence, 156
information fusion design, 513–514
Topographic Engineering Center (TEC), image and spatial data fusion, 90
Total object mass and belief/plauasibility intervals, combat identification (CID) mapping, identification information measurement, 791–794
Trace detection limits (TDLs), chemical and biological sensors, quantitative/qualitative analysis, 749–751
Track assignment, top-down data fusion automation, 160
Track-before-detect, likelihood ratio detection and tracking, 296
Track coincidence, top-down data fusion automation, 156
Track initiation, multiple-frame assignments, 306–307
Track maintenance, multiple-frame assignments, 307–308
Track splitting, multiple-target tracking and, 74–75
Track-to-track data fusion, covariance intersection, 339
Tracker-Correlator problem space, 598–599
image and spatial data fusion, 94
particle filter samples, 191–199
top-down data fusion automation, 161–162
Tracking rate, particle filtering, 194–199
Training data, multisensor data fusion, 12–13
Trajectory measurements, multiple-target tracking and, 72
Transformation matrix, data registration, 120–122
Transformations of requirements for information process (TRIP), resource management and data fusion model, 37–43
Transform derivation, three-dimensional imagery enhancement, 101
Transportation applications, image and spatial data fusion, 92
Triangulated irregular networks (TIN), historical overview, 621
Trigger sensing, information fusion design, 513
Turbine engine lubrication system (TELSS), condition-based maintenance
operational data, metasensor processing, 724–726
simulation model and metasensors, 722–723
Two-dimensional data structures, database management systems, spatial representation performance, 631–634
Two-dimensional map-based representation, database management systems, 622–625
Two-dimensional performance, hypothesis selection, 611–615
Unambiguous correlation, characteristics, 599
Unambiguously generated ambiguous (UGA) measurements
Bayes-invariant transformations, 387–388
generalized likelihood functions, 385–386
Unambiguously generated unambiguous (UGU) measurements, Bayesian filtering, 380–381
Unattended ground sensors (UGS), top-down data fusion automation, 155
combat identification (CID), 775–776
identification friend-or-foe (IFF) uncertainties, 794–797
Shannon communication analysis, 789–794
information fusion design, needs prioritization, 508
multiple-target tracking and, 70–72
multisensor data fusion, 12
nonlinear data fusion transformation, 348–349
situation and threat assessment, 455–457
Unified multiple-target tracking
multiple-hypothesis tracking, 288–289
multisource integration, 403
Uniform sampling, data representation accuracy, 626–627
Army Battle Command System, 764–766
Unscented filter (UF), nonlinear data fusion, 356–358
high-order nonlinear system, 361–363
multiple-level sensor fusion, 363–365
discontinuous transformation, 354–356
polar-to-Cartesian coordinates, 353–354
properties, 352
sigma points, 351
Unscented (transform) Kalman filter (UKF), particle filtering, 178
Unstable regions, Project Correlation, 599
Update function, particle filtering resampling, 186–187
Usability evaluation, information fusion design, display/interface, 527–529
User actions, information fusion design, 515–517
User-algorithm fusion, assisted target identification, 529–530
User control, information fusion design, 517–518
assisted target identification, user-algorithm fusion, 529–530
cognitive fusion, 509–510, 532–527
contextual information, 508–509
data fusion information group model, 510–512
dual data fusion & resource management (DF&RM) models, 65–66
future research issues, 530–532
performance evaluation, 520–529
display/interface design, 527–529
dynamic decision making, 523–526
metrics, 522
situational awareness, 520–522
work analysis/task analysis, 526–527
revised JDL data fusion model, 58–59
TRIP resource management and data fusion model, 39–43
Utility assessment, dual data fusion & resource management (DF&RM) models, 65–66
Utility theory, multisensor data fusion, 11
Vacuum cleaner approach, military intelligence and, 537–539
Vector formation, combat identification (CID) mapping, 788–789
Velocity calculations, multiple-target tracking, nearest-neighbor rule limitations, 73–74
VHF omni-directional range (VOR), multisensor data fusion, 2–3
Vibration analysis, condition-based maintenance, industrial gearbox example, 712–713
Viral detection, biological sensors, 747–749
Army Battle Command System disaster management assessment, 770
human-computer interaction, 539–541
Virtual sensor data, condition-based maintenance, 730–734
Army Battle Command System disaster management assessment, 770
revised JDL data fusion model, 58–59
VITEC system, information fusion and, 530–532
Voting, decision-level fusion, condition-based maintenance, 706
Vulnerability, ontology, 464–465
Waterfall systems engineering, requirements derivation, data fusion, 552–553
Wavelet-based data registration, range images, 124–127
Wavelet encoding methods, dynamic imagery data fusion enhancement, 100
Web ontology language (OWL), situation and threat assessment, 462
Weighted decision fusion, condition-based maintenance, 706–707
Whispering in the hall problem, decentralized data fusion, 321–323
Work analysis/task analysis, information fusion design, 526–527
Zero index sets, multiple-frame assignments, 304