A
Ad hoc observers, information, 1–2
AI, see Artificial intelligence (AI)
factorial experiment, 402
ANOVA, see Analysis of variance (ANOVA)
Architectures, distributed estimation
appropriate architectures selection, 104–105
fusion graph, see Fusion architecture graph
information communicated and common prior knowledge, 104
measurements and processors, 97–98
sensors, processors and users, 97
Artificial intelligence (AI), 287–288
B
Bar-Shalom-Campo and Speyer fusion rules
cross-covariance, 130
fused estimation error, 130
likelihood function, 130
naïve fusion rule, 130
repeated track fusion, see Repeated track fusion
simple convex combination rule, 130
Bayes filter
Markov transition density, 203
multisensor measurements, 204
normalization factor, 203
Bayesian distributed fusion algorithm, 259–260
equation, 107
Gaussian random vectors, see Gaussian random vectors
goal and measurements, 105
node 1 and 2, 106
private and common information, 106
probability distribution, discrete variable, 105–106
Bayesian maximum-likelihood fusion (BML) rule
likelihood function, 133
weight matrices, 133
BDI, see Belief–desire–intention (BDI)
communication and coordination, 445–446
description, 445
practical reasoning, 445
Belief network (BN) model
junction tree, 271
situation assessment, 273, 274
Bounded covariance inflation
description, 20
inflated covariance matrix, 22
joint covariance matrices, 20
Kalman filter update equation, 21
linear transform, 21
upper and lower bounds, 22
Burst communications, 180
C
definition, copymachine1, 454
description, 453
homography matrix, 453
point correspondence, offline camera, 453, 454
Cardinalized PHD (CPHD) filter fusion
known double-counting, 227
measurement-update equations, 219–220
T2F, independent sources, 224–226
T2F, mathematical derivations, 234–235
time-update equations, 218–219
CCA, see Continuous combinatorial auction (CCA)
Central processing, 126
asynchronous operation, 169, 170
cache vs. filter algorithms, 167–168
common and contributed information, 168
common information vs. contributed information, ideal pair, 169
description, 167
miscommunication, 169
CI, see Covariance intersection (CI)
Classifier fusion
algorithm-combining approach, 255
MAP, 255
methods, taxonomy, 254
product rule, 256
taxonomic categorization, 253
Closest point of approach (CPA), 301
Common tactical picture (CTP), 272, 276
Communication and decentralized data fusion (DDF)
channel filter approaches, see Channel filter
dynamic systems, see Dynamic systems, DDF
global agreement, nodes, 170
log-likelihood/information form, 169
tree network topology, channel cache, 165–167
Communications algorithm, k-tree
data-tag set elimination, 191
description, 190
separator, 190
local measurement error covariance matrices, 141
noise intensity level and initial state accuracy, 142–143
normalized initial position standard deviation, 142, 143
normalized process noise intensity, 141, 142
position estimation performance, 142
Consistent tactical picture (CTP), 401
Contextual enhancement, tracking
accuracy, system, 456
communicate fusion estimate dialog, 458
expected occlusion situation, 458
Continuous combinatorial auction (CCA), 370
Chair-Varshney rule, 83
copula theory, 84
description, 81
LRTs, 82
marginal distributions, 83
M-ary hypothesis, 83
ratio-based tests, 82
suboptimal binary quantizers, 83
Chen-Arambel-Mehra fusion rule, 135
Chernoff fusion rule, 134
cross-covariance matrix, 133–134
error hyper-ellipsoids, 208
fused target-localizations, 209
Speyer fusion rule, 134
CPA, see Closest point of approach (CPA)
CTP, see Common tactical picture (CTP); Consistent tactical picture (CTP)
D
DARPA, see Defense Advanced Research Projects Agency (DARPA)
Data fusion and resource management (DF&RM), 380
DBNs, see Dynamic Bayesian networks (DBNs)
DDF, see Decentralized data fusion (DDF); Distributed data fusion (DDF)
Decentralized data fusion (DDF)
characterization, constraints, 162
and communication, see Communication and decentralized data fusion (DDF)
communications latencies and failures, 193
description, 162
dynamic systems, see Dynamic systems, DDF
k-tree topologies, redundant and dynamic networks, see K-tree topologies
marginalization, information form, 193–194
trajectory information form equivalence, 194–196
Defense Advanced Research Projects Agency (DARPA)
social network utilization, 351
web information and tools, 353
Delayed and asequent observations
description, 179
destructive prediction estimation, 179
N timesteps, 179
prediction step, 178
small delay, 180
trajectory information matrix, 179
factors and interaction complexity, 397, 398
statistical, 395
test-planning methods, 396
Deterministic dynamics, object tracking, 117–118
Developmental test & evaluation (DT&E)
bounded system, 384
engineering design goals, 385
types, testing, 384
DF&RM, see Data fusion and resource management (DF&RM)
Distributed data and information fusion systems (DDIFS), see Test and evaluation (T&E), DDIFS
applications, 1
APPs and HCI, 2
cognitive and information processes, 6
content, 18
design, 19
Endsley’s situation awareness model, 8, 9
information recycling, see Information recycling
JDL, see Joint Directors of Laboratories (JDL)
mathematical sense meaning and redundant observations, 2
military applications and intelligence, 8
multiple observers/sensors, 2–3
net-centric generation, 4
OODA, 8
probabilistic model, computational trust, 35
sensor coordination, see Sensor coordination
stakeholders, see Stakeholders
state estimation, 2
traditional information, 2
Distributed detection, wireless sensor networks
binary distributed detection, 66
decision-making structures, 65
decision theory/hypothesis testing, 65–66
FDR-based decision, 89
ideal communication channels, see Ideal communication channels
nonideal communication channels, see Nonideal communication channels
ROI, 90
Distributed estimation
architectures, see Architectures, distributed estimation
Bayesian distributed fusion algorithm, see Bayesian distributed fusion algorithm
fusion architecture and best performance, 96
Gaussian distributions/error covariances, see Error covariances, distributed estimation
MAP, BLUE and cross-covariance fusion, 121
multiple sensors, 96
object classification, see Object classification
optimal Bayesian distributed fusion, see Optimal Bayesian distributed fusion
optimal fusion algorithm, 121
suboptimal Bayesian distributed fusion algorithms, see Suboptimal Bayesian distributed fusion
Distributed fusion environments
centralized, hierarchical, peer-to-peer and grid-based, 277
network, distributed fusion nodes, 276
process observations, 274
Distributed high-level fusion
algorithm, situation assessment, 277–282
BN model, situation assessment, 273, 274
decentralized processing environment, 273
distributed fusion environments, 274–277
distributed Kalman filter, 282–285
GIG, 273
role, intelligent agents, 286–290
SA, 271
Distributed Kalman filter
fusion nodes, 285
JSTARS, 283
target tracking with and without feedback, 284
UAV, 283
Distributed processing, 126
Distributed situation assessment, algorithm
junction tree, 279
junction tree construction and inference, 280–282
pairwise communication-link information, 278
sensor network, 278
spanning tree, 278
DNN, see Dual node network (DNN)
DOE, see Design of experiments (DOE)
Double-counting
CPHD/PHD filter distributed fusion, 227
multitarget distributed fusion, 223
T2F, single-target distributed fusion, 206–207
DT&E, see Developmental test & evaluation (DT&E)
Dual node network (DNN), 380
Dynamic Bayesian networks (DBNs), 311, 312
application, trajectory state approach, 177
burst communications, 180
common process noise problem, 177–178
delayed and asequent observations, 178–180
delayed states, 171
filtering, stored filter, 181–182
filtering, trajectory state system, 181
observation/communication interruptions, 171
operation, channel caches and trajectory states, 183
solution, trajectory states, 180–181
state dynamics, see State dynamics
trajectory state formulation, 183
E
Electronic support measure (ESM), 374–375
EPP, see Expected posterior probability (EPP)
Error covariances, distributed estimation
cross-covariance fusion, 116–117
posteriori fusion/best least unbiased estimate, 115–116
ESM, see Electronic support measure (ESM)
Expected posterior probability (EPP)
classification vs. number of communications, 267, 268
classification vs. time, 264, 265
object class separation vs. correct classification, 264, 266
transition probability vs. classification, 266, 267
Exponential mixture (XM) fusion
CPHD filter distributed, 228–229
multitarget distributed, 223–224
particle-PHD tracks, T2F, 234
PHD filter distributed, 229–230
single-target distributed fusion, 209–212
F
defined, 78
description, 76
distributed detection system, 79–81
MCPs, 77
nonidentical decision thresholds, 76
SNRs, 76
FDR, see False discovery rate (FDR)
Finite-set statistics
multitarget recursive Bayes filter, 212–214
PHD filters
constant-gain Kalman filters, 216
Markov transition density, 217
SMC, 218
sensor-bias estimation, 221
SLAM, 220
Formal experimental design and statistical analyses
dominant analysis methodology, 395
factors and interaction complexity, DOE strategies, 397, 398
Monte Carlo replications, 394–395
notional layered experimental design, 396, 398
statistical experimental design, 393–394
stochastic properties, 393
topological structure, 394
types, DOE techniques, 396–397
Fusion
“a priori” and “a posteriori”, 59
characterizations, sense-making, 60
computer-based information fusion, 57
nonmonotonic logic, 59
description logics, 450
HLIF and low-level tracking refinement, 452
knowledge model structure, 450
low-and high-level fusion and feedback, 450, 451
top-down rules, 452
update situation knowledge messages, 449
Fusion algorithms
EPP, see Expected posterior probability (EPP)
RMS, see Root mean square (RMS)
multiply connected fusion, 99–100
singly connected fusion, 98–99
Fusion rules
Chair-Varshney, 72
composite hypotheses, 72
description, 71
LRT, 72
SR noise, 73
UMP and GLRT, 73
Fusions system
identification, domain complexities, see Intelligence analysis
system design, development and evaluation, see Intelligence analysis
G
Gaussian random vectors
description, 107
error covariance and filter equations, 108–109
fusion equation, 107
fusion node observation equation, 108
information matrix fusion equations, 109
Generic Hub (GH) data model, 330
GIG, see Global information grid (GIG)
Global information grid (GIG), 273, 286
H
HACs, see Human-agent collectives (HACs)
human-system interaction, 421
system architecture, 423
HCI, see Human-computer interaction (HCI)
inflated independent beta distributions, 37–38
Kalman filter trust model, 38–39
Hidden Markov Models (HMMs), 312
Hierarchical architecture
fusion with feedback, 110
fusion without feedback, 109
Hierarchical task network (HTN), 308
High-level information fusion (HLIF); see also Fusion agents
CI and DIF, 442
context knowledge, visual IF, 442
description, 441
IF systems, 441
MPEG-7, 442
OWL ontology, 443
RACER, 443
SNAP and SPAN, 443
VERL and VEML, 443
HMMs, see Hidden Markov Models (HMMs)
HTN, see Hierarchical task network (HTN)
Human-agent collectives (HACs)
individual and collective goals, 42
Human-computer interaction (HCI), 2
Human engineering factors
automated processes, 409
characterization, human-fusion system interaction, 410, 411
cognitive system engineering, 430
design and development, 431
human-system integration, 409
identification, fusions system, 418–421
military intelligence analysis, 412
system design, development and evaluation, 412–418
touch points, hard-soft process, 421–430
Hybrid sensing/hybrid cognition, SOA
artificial intelligence and data fusion algorithm, 361
service-oriented system methodologies, 361
social networks, 361
I
Ideal communication channels
Bayesian Formulation, see Bayesian Formulation
correlated decisions, see Correlated decisions
decision rule partitions, 68
false discovery rate-based sensor decision rules, Sensor decision rules
hypothesis testing problem, 67
joint density, 69
Neyman–Pearson Formulation, see Neyman–Pearson Formulation
nonidentical decision rules, 69
parallel configuration, 68
Indoor surveillance, VSNs
communicate-fused estimation dialog, 452
contextual enhancement, tracking, see Contextual enhancement, tracking
continuous surveillance, 452
framework configuration, see Camera calibration
low-level information fusion, see Low-level information fusion
scene interpretation, 459
update situation knowledge dialog, 452
Inflated independent beta distributions, 37–38
Informational transactions, 127
Information graph
distributed architectures, 103–104
multiply connection, hierarchical fusion, 101–103
singly connected graph, singly connected fusion architectures, 101
Information recycling
bounded covariance inflation, 20–22
decentralized tracking, 23, 24
Information-sharing strategy (ISS), 383
description, 9
“invisible” computers, 9
technology trends impacts, data fusion, 9–11
traditional sensing/computing networks, 1
complexities and fusion system capabilities, 418–420
decision biases, 418
hard-soft fusion process, see Hard-soft fusion process
information processes, 418
military, 412
nonexhaustive factors, 413, 416–417
stages and extended capabilities map, 421, 422
system/process documentation, 430
Intelligent agents
agent-based application, 286
agent properties and data fusion, 287, 288
AI, 287
decentralized data fusion system, 288
graphical Bayesian belief networks, 289
knowledge-based, 290
MADSNs, 290
military hierarchical organizations, 291
NCW, 287
real-time distributed tracking, 289
ISS, see Information-sharing strategy (ISS)
IT, see Information technology (IT)
J
JDL, see Joint Directors of Laboratories (JDL)
Joint Directors of Laboratories (JDL)
ASAS, 4
defined high-level processes in, 5–6
description, 4
subprocesses and functions, 5
target’s kinematics, 6
top level model, 4
Joint multitarget (JoM) estimator, 214
Joint surveillance target attack radar system (JSTARS), 283
JoM estimator, see Joint multitarget (JoM) estimator
JSTARS, see Joint surveillance target attack radar system (JSTARS)
K
Kalman filter trust model, 38–39
K-tree topologies
allowable links, 186
communications algorithm, see Communications algorithm, k-tree
data-tagged decentralized algorithm, 192
DDF on, 187
description, 184
fully connected topology, 186–187
link and node failure robustness, 191–193
local neighborhood property, 189–190
scalability and correctness, 184
spanning-tree algorithms, 184
treewidth, graph, 186
L
Link and node failure robustness, 191–193
Low-level information fusion
fused tracking, fusion agent, 456, 457
ground-truth positions, 456, 457
HLIF knowledge, 456
local tracking, sensor agents, 455, 456
tracking information, 456
M
MADSNs, see Mobile agent-based distributed sensor networks (MADSNs)
MAP, see Maximum posterior probability (MAP)
Marginalization, information form, 193–194
Market architecture for sensor management (MASM)
market-oriented programming techniques, 368
models network resource, 369
optimal bidding strategy, 371
optimal resource allocation, 370
target destruction, 373
tatonement process, 369
Widrow–Hoff learning rule, 374
Market-oriented programming
CCA, 370
current error, 374
deterministic optimization, 372
market algorithms, resource allocation, 368
MASM, 368
multiperiod optimization, 372
optimal resource allocation, 371
sensor networks, 371
target destruction, 373
Tatonement, 369
Markov transition density, 217
MAS, see Multi-agent systems (MAS)
MASM, see Market architecture for sensor management (MASM)
Maximum posterior probability (MAP), 255
Max-sum algorithm
art approximate algorithms, 27
decentralized coordination algorithm, 27
defined messages, 26
description, 25
factor graph, 25
function to variable, 26
sensor network, 25
variable node, 26
variable to function, 26
Measurement-to-track fusion (MTF), 200
Measures of Effectiveness (MOEs), 358
Measures of Performance (MOPs), 382
Military operations, threat analysis
operational environment, 318
predictability of the behavior, 317
susceptibility to coercion, 317
symmetry, 318
task complexity, 316
uncertainty, 316
Minimum-variance (MV) fusion rule, 132
Mobile agent-based distributed sensor networks (MADSNs), 290
MOEs, see Measures of Effectiveness (MOEs)
MOPs, see Measures of Performance (MOPs)
MTF, see Measurement-to-track fusion (MTF)
computational models, trust, 34
data fusion and decision-making node, 18
stakeholders, 18
autonomous and social abilities, 444
BDI, see Belief–desire–intention (BDI)
detected objects, 444
FIPA ACL messages, 448
fusion agents, see Fusion agents
high-level hierarchical and partially distributed architecture, 446, 447
ontologies, 444
sensor agents, see Sensor agents
standard communication protocols, 444
Multiple-target tracking problems, 125
banded matrix, 175
information matrix and vector, 174–175
nonadditive form, 176
timestep, 176
total information, trajectory system, 175
trajectory state system propagation, 176
Multitarget calculus
integral-differential, 214
multitarget probability distribution, 216
Poisson process, 215
Multitarget distributed fusion
known double-counting, T2F, 223
T2F, independent sources, 222–223
Multitarget recursive Bayes filter
cardinality distribution, 213
defined, 213
JoM, 214
Multitarget T2F
known double-counting, 223
N
Network-centric concepts
description, 47
operational advantages, 48
self-organization and self-synchronization, 60–61
“share-ability”, 48
value of information, decision-making, 51–52
Network-centric warfare (NCW)
cognitive domain, 286
GIG, 286
information domain, 285
physical domain, 285
Network value chain, measures and metrics
degree dimensions-to-attributes and measures/metrics, 386, 388
NCO, 386
quality and degree measures, 386, 387
Neyman–Pearson Formulation, 70–71
Nondeterministic dynamics, object tracking
augmented state vector and approximation, 119
cross-covariance, single time, 119
Nonideal communication channels
Chair-Varshney fusion rule, 86
distributed detection and no channel state information, 88–89
distributed detection and partial channel state information, 87–88
MRC fusion rule, 86
optimal likelihood ratio-based fusion rule, 85–86
signal model and fusion center, sensor, 85
WSNs, LPI/LPD, 84
Nonmyopic sensor management
defined, 365
market-oriented programming, 368–374
network resources policies, 365
PE, simulation test bed, 374–376
stochastic dynamic programming, 366–368
Nontemporal probabilistic approaches, threat analysis
Bayesian network, 310
decision trees, 309
sensitivity analysis, 311
Normalized standard error (NSE), 41
NSE, see Normalized standard error (NSE)
O
Object classification
declaration fusion process, 248
discriminative approaches, 247, 248
explicit double-counting, 251
generative approaches, 247, 248
imaging techniques, 259
implicit double-counting, 251–252
information-sharing strategies, 250
legacy systems, 252
mathematical/formal integrity, 250
mixed uncertainty representations, 253
NCTR methods, 247
notional multisensor object classification process options, 248, 249
support vector machines, 247
types, data, 250
Object tracking
deterministic dynamics, 117–118
nondeterministic dynamics, 118–119
Observe-orient-decide-act (OODA), 7, 8
OGC, see Open Geospatial Consortium (OGC)
Bar-Shalom-Campo and Speyer fusion, 129–130
BML rule, see Bayesian maximum-likelihood fusion (BML) rule
calculation, cross-covariance matrix, 133–134
characterization, Ornstein-Uhlenbeck model, 138
CI methods, see Covariance intersection (CI)
complementary sensor case, see Complementary sensor
constant-velocity model/small-white-noise model, 137
estimation error covariance, 129
fusion rules, 129
Gaussian approximation, 128
joint probability density function, 128
linear combination, 129
linear Gaussian estimation, 128
local data processor, 128
local estimation error, 128
maneuvers approaches, 139
MV fusion rule, see Minimum-variance (MV) fusion rule
Ornstein-Uhlenbeck model, 137
supplementary sensor case, see Supplementary sensor
zero-mean Gaussian random vector, 127
Ontological structures, distributed fusion
annotation, regions and objects, 339, 340
computer domain, 329
embargoed Port situation, 343
geographical feature ontology, 338, 339
GH, 330
inferring relevant repositories, 336–337
information annotation and processing, 341
information integration, 328
information producers and consumers, 327, 328
interoperability and inference, 334–336
ontological reasoning, 328
OWL, 330
types, information sources, 327
UML, 330
Ontology web language (OWL), 442–443
OODA, see Observe-orient-decide-act (OODA)
Open Geospatial Consortium (OGC), 353
Optimal Bayesian distributed fusion
arbitrary distributed fusion architecture, 110–111
hierarchical architecture, 109–110
Optimal Bayesian object classification
centralized algorithm, 258
communication, local agent, 261
comparison, fusion algorithms, 264–268
DBN, 257
distributed fusion algorithm, 259–260
extrapolation, high-level agent, 261–262
fusion, high-level agent, 262
performance evaluation approach, 263–264
probability distance measures, 263
sensor measurements, 262
simulation scenario and data generation, 263
Optimality track fusion
covariance matrix, 136
description, 135
extrapolation step, 136
Koch-Govaers fusion rule, 136
linear-Gaussian systems and off-line information, 137
local variance matrices, 136
MAP, 135
OWL, see Ontology web language (OWL)
P
Participatory sensing and sensor webs
data networks, 349
information fusion community, 354
OGC, 353
PEIR, 350
SPS, 353
TML, 353
PDA, see Probabilistic Data Association (PDA)
PE, see Performance evaluation (PE)
PEIR, see Personal Environmental Impact Report (PEIR)
data fusion and resource management architecture, 389–390
MASM, 375
Pareto optimal front, 393
T&E system, 390
Personal Environmental Impact Report (PEIR), 350–354
Person-by-Person optimization (PBPO) approach, 70
PHATT, see Probabilistic Hostile Agent Task Tracker (PHATT)
automation, 307
dynamic process, hypotheses formulation, 304
evolution, time, 315
goal recognition, 305
HTN plan representation, 308
mental state modeling, 314
model manipulation, 315
nontemporal probabilistic approaches, 309–311
perception, problems, 306
plan revision, 315
probabilistic approaches, temporal dimension, 311–314
symbolic approaches, 309
Probabilistic Data Association (PDA), 357
Probabilistic Hostile Agent Task Tracker (PHATT), 313
Probability hypothesis density (PHD) filters
independent sources, T2F, 226
known double-counting, 227
T2F fusion, mathematical derivations, 236
Q
Querying technology
information producers, 331
logic-based systems, 331
MetaCarta’s technology, 331
natural language expression, 330
SQL, 331
R
RACER, see Renamed Abox and Concept Expression Reasoner (RACER)
Renamed Abox and Concept Expression Reasoner (RACER)
inference engine, 450
scene interpretation, 443
SURV ontology, 454
Repeated track fusion
architectures, distributed tracking systems, 143
categorization, fusion rules, 144
description, 143
estimation error covariance matrices and sensors, 144
with feedback
Bar-Shalom-Campo, Speyer and CI rules, 149–150
description, 148
MV fusion rule, 150
normalized process noise intensity, 149
optimal distributed fusion algorithm, 150
tracklet rule, 150
without feedback
Bar-Shalom-Campo, Speyer and CI fusion rules, 145
decorrelation form, 148
and decorrelation method, 146
deterioration, distributed tracking, 148
informational transactions, 145
information graphs, processing architectures, 144
normalized process noise intensity, 147
Ornstein-Uhlenbeck model, 147
RMS, see Root mean square (RMS)
classification probability error vs. object class separation, 264, 266
defined, 264
probability error vs. average number of communications, 267, 268
time vs. classification probability error, 264, 265
transition probability vs. classification probability error, 266, 267
S
Semantic information services (SIS), 327–328
Sense-making
“community of interest”, 57
definitions, 53
frame-building process, 53
problem characteristics, 54, 56
process characterization, 57–59
utility-type function, 54
Sensor coordination
description, 24
Sensor Planning Service (SPS), 353
Sequential Monte Carlo (SMC), 218
Service-oriented architecture (SOA)
distributed human-centric information fusion, 348
distributed sensors, participatory sensing, 348
GPS, 347
high-level assessments, 354–359
hybrid sensing/hybrid cognition, 360–361
information fusion community, 348
mobile device usage, 347
participatory sensing and sensor webs, 349–354
pyramid, see Service-oriented fusion pyramid
Service-oriented fusion pyramid
human-centric information fusion, 355
Simultaneous localization and mapping (SLAM), 220
Single-target distributed fusion
covariance intersection, 207–209
exponential mixture fusion, 209–212
independent sources, T2F, 204–205
T2F, known double-counting, 206–207
SIS, see Semantic information services (SIS)
Situation assessment (SA)
BN model, 272
NCW, 271
Situation theory ontology (STO), 341, 342
SLAM, see Simultaneous localization and mapping (SLAM)
SMC, see Sequential Monte Carlo (SMC)
SOA, see Service-oriented architecture (SOA)
SPS, see Sensor Planning Service (SPS)
SQL, see Structured query language (SQL)
Stakeholders
communication network, 30
computational mechanism, 29
governmental and nongovernmental organizations, 29
MAS, 31
multisensor network target tracking, 30
ROI, sensor, 34
sensor network system and communication allocation, 34, 35
sensor-net-work topology, 31
State dynamics
dynamic transformation, 172
equivalence, conventional approach, 173–174
linear discrete time state dynamic model, 172
multiple trajectory states, see Multiple trajectory states
trajectory information approach, 172–173
trajectory state approach, 171
STO, see Situation theory ontology (STO)
Stochastic dynamic programming
adaptive Lagrangian relaxation, 367
approximation techniques t, 367
Bellman’s optimality principle, 366
research approach, 368
sensor measurements, 367
Structured query language (SQL), 331
Suboptimal Bayesian distributed fusion
channel filter fusion, 112–113
naïve fusion, 112
Supplementary sensor
Bar-Shalom-Campo rule, 141
BML rule, 140
conditional probability density, 140
description, 139
inter-sensor cross-covariance matrix, 140
normalized initial position standard deviation, 140, 141
normalized process noise intensity, 139, 140
process noise intensity and stationary velocity covariance, 141
T
Temporal probabilistic approaches, threat analysis
HMMs, 312
PHATT, 313
types, 311
Test and evaluation (T&E), DDIFS
complexities, error audit trails, 393
CTP, 401
DF&RM, 380
DNN, 380
DT&E, 384
experimental design, 381
formal experimental design and statistical analyses, 393–398
fusion estimates and truth states, 388–389
information fusion processes and algorithms, 404
inter-tier(tiers1and2), ANOVA, 402, 406
ISS, 383
MOPs, 382
PE process, 382
“production prototype” program, 381
SOA, 386
software testing, 385
statistical/mathematical analysis techniques, 379
subjectively judged properties, 383, 384
two-aircraft configuration, 399, 400
two vs. six offensive sweep scenario, 399, 400
Theoretical foundation, distributed fusion
CPHD/PHD filter distributed fusion, 224–230
finite-set statistics, 212–221
mathematical derivations, 234–241
MTF, 200
multi-Bernoulli filters, 201
multitarget distributed fusion, 222–224
Pedigree techniques, 201
single-target distributed fusion, 202–212
Threat analysis, distributed environments
action, event and reference point, 298–299
advantages, 320
analytical challenges, 321–322
capability indicators, 302
centralized and decentralized control, 319
collaboration challenges, 322
CPA, 301
dual perspective, 303
goal and plan recognition, 304–306
impact assessment, 300
intent indicators, 302
and network-centric operation, 322–323
operational challenges, 320–321
opportunity indicators, 302
plan recognition, see Plan recognition
reasoning processes, 296
TML, see Transducer Markup Language (TML)
Track association
Bar-Shalom metric, 153
Chong-Mori-Chang metric, 153–154
CI metric, 153
expanded state metric, 154
sensor biases and the track association, 155
Singer-Kanyuck metric, 152
Track fusion
one-time, see One-time track fusion
repeated, see Repeated track fusion
Track-to-track fusion (T2F)
CPHD filter distributed fusion, 224–226
general multitarget distributed fusion, 222–223
known double-counting, 206–207
multitarget distributed fusion, 222–223
particle-PHD tracks, 232
PHD filter distributed fusion, 226
Trajectory information form equivalence, 194–196
Transducer Markup Language (TML), 353
Tree network topology, channel cache
algorithm, 166
DDF, 166
description, 165
disjoint subsets, 166
observation and communication cache, 167
transmission, communication term, 167
Trust and reputation
effective models, 35
evaluation
covariance matrix and normalized error, 40
estimated expected utility, 41
expected information content vs. NSE, 40, 41
Kalman filter encode, 40
reputation system, 39
simulation run, 40
single-dimensional trust models, 42
expected utility, contract, 35–37
heterogeneous contracts, see Heterogeneous contracts
U
UAV, see Unmanned aerial vehicle (UAV)
UML, see Universal Modeling Language (UML)
Universal Modeling Language (UML), 330
Unmanned aerial vehicle (UAV), 282–283
V
Value chain
individual nodes, 51
ISS, 50
multilayered process, 49
NCW conceptual framework, 50, 51
self-organization and self-synchronization, 60–61
VEML, see Video event markup language (VEML)
VERL, see Video event representation language (VERL)
Video event markup language (VEML), 443
Video event representation language (VERL), 443
classification, 439
communication, 437
context-based approaches, HLIF, see High-level information fusion (HLIF)
description, 436
DETER system, 440
DIF software architectures and techniques, 441
disadvantage, classical systems, 441
indoor surveillance, see Indoor surveillance, VSNs monitoring and surveillance tasks, 436
multi-agent architectures, 441
multi-agent systems, see Multi-agent systems, VSNs
object detection, 438
process enhancement, 439
reliability and accuracy, 436–437
requirements and issues, 437
scalability, 440
third-generation surveillance systems, 440
third-generation video systems, 436
VSAM, 439
VSNs, see Visual sensor networks (VSNs)
X
XM fusion, single-target distributed fusion
model, 210
multidimensional Gaussian distribution, 209
optimization approaches, 212
Wasserstein distance, 211