Auto-regressive models, linear prediction methods 3—5 13—14
Auto-regressive models, linear prediction methods, auto-regressive moving-average models versus 14—15
Auto-regressive moving-average models see "Linear prediction methods frequency
Banzhaf sonograms, neural networks 319—323
Bayes' classifier, known channels 349—350
Beamformer, coherent signals 282—283
Blackman — Tukey methods, frequency estimation 2—3
Blind adaptive MAP symbol detection 339—405
Blind adaptive MAP symbol detection, channel and signal models 344—345
Blind adaptive MAP symbol detection, conclusions 396—397
Blind adaptive MAP symbol detection, introduction 341—344
Blind adaptive MAP symbol detection, metric pruning 380—382
Blind adaptive MAP symbol detection, reduced complexity MAPSD algorithms 365—382
Blind adaptive MAP symbol detection, reduced complexity MAPSD algorithms, LMS adaptation 366—372
Blind adaptive MAP symbol detection, reduced complexity MAPSD algorithms, LMS adaptation, fading channels 370—372
Blind adaptive MAP symbol detection, reduced complexity MAPSD algorithms, LMS adaptation, fading channels, simulation results 372
Blind adaptive MAP symbol detection, reduced complexity MAPSD algorithms, LMS adaptation, time-invariant channels 366—367
Blind adaptive MAP symbol detection, reduced complexity MAPSD algorithms, LMS adaptation, time-invariant channels, simulation results 367—370
Blind adaptive MAP symbol detection, reduced complexity MAPSD algorithms, metric pruning 380—382
Blind adaptive MAP symbol detection, reduced complexity MAPSD algorithms, suboptimal MAPSD with decision feedback 372—380
Blind adaptive MAP symbol detection, reduced complexity MAPSD algorithms, suboptimal MAPSD with decision feedback, algorithm 374—376
Blind adaptive MAP symbol detection, reduced complexity MAPSD algorithms, suboptimal MAPSD with decision feedback, algorithm, simulations 376—379
Blind adaptive MAP symbol detection, reduced complexity MAPSD algorithms, suboptimal MAPSD with decision feedback, feedback algorithm 379—380
Blind adaptive MAP symbol detection, sequence estimation versus symbol detection 346—351
Blind adaptive MAP symbol detection, sequence estimation versus symbol detection, Bayes' classifier for known channels 349—350
Blind adaptive MAP symbol detection, sequence estimation versus symbol detection, MAP decoders for known channels 350—351
Blind adaptive MAP symbol detection, sequence estimation versus symbol detection, ML and MAP sequence estimation 346—347
Blind adaptive MAP symbol detection, sequence estimation versus symbol detection, optimal MAPSD for known channels 347—349
Blind adaptive MAP symbol detection, TDMA signal recovery, dual-mode algorithm 382—396
Blind adaptive MAP symbol detection, TDMA signal recovery, dual-mode algorithm, EKFs on MAPSD filter bank 390—391
Blind adaptive MAP symbol detection, TDMA signal recovery, dual-mode algorithm, measurement model with timing offset 384—387
Blind adaptive MAP symbol detection, TDMA signal recovery, dual-mode algorithm, simulations 391—396
Blind adaptive MAP symbol detection, TDMA signal recovery, dual-mode algorithm, training mode adaptation of auxiliary EKF 387—389
Blind adaptive MAP symbol detection, time updates for blind MAPSD algorithm 398—400
Blind adaptive MAP symbol detection, unknown channels 352—365
Blind adaptive MAP symbol detection, unknown channels, blind algorithms for fading channels 364—365
Blind adaptive MAP symbol detection, unknown channels, blind MAP sequence estimation 352—353
Blind adaptive MAP symbol detection, unknown channels, blind MAP symbol detection 354—358
Blind adaptive MAP symbol detection, unknown channels, blind Viterbi algorithm 358 360
Blind adaptive MAP symbol detection, unknown channels, computer simulations 358 360—364
Burg algorithm, frequency estimation 11—13
Chaotic signal analysis, higher order statistics 105—154
Chaotic signal analysis, higher order statistics, bispectral analysis 142—149
Chaotic signal analysis, higher order statistics, bispectral analysis, bicoherence 144
Chaotic signal analysis, higher order statistics, bispectral analysis, definitions and properties 143—144
Chaotic signal analysis, higher order statistics, bispectral analysis, examples 146—149
Chaotic signal analysis, higher order statistics, chaos, characterizations 112—113
Chaotic signal analysis, higher order statistics, chaos, characterizations, attractor 112
Chaotic signal analysis, higher order statistics, chaos, characterizations, spectrum 113
Chaotic signal analysis, higher order statistics, chaos, characterizations, unpredictability 113
Chaotic signal analysis, higher order statistics, chaos, definitions 111—112
Chaotic signal analysis, higher order statistics, chaos, examples 113—117
Chaotic signal analysis, higher order statistics, conclusion 149
Chaotic signal analysis, higher order statistics, cumulants of random variables 107—111
Chaotic signal analysis, higher order statistics, cumulants of random variables, definitions 107
Chaotic signal analysis, higher order statistics, cumulants of random variables, estimation issue 110—111
Chaotic signal analysis, higher order statistics, cumulants of random variables, linear transformation 110
Chaotic signal analysis, higher order statistics, cumulants of random variables, moments 108—110
Chaotic signal analysis, higher order statistics, cumulants of random variables, properties 108
Chaotic signal analysis, higher order statistics, dimension estimation 126—142
Chaotic signal analysis, higher order statistics, dimension estimation, correlation dimension 126—129
Chaotic signal analysis, higher order statistics, dimension estimation, rank dimensions 129—134
Chaotic signal analysis, higher order statistics, dimension estimation, rank dimensions, Karhunen — Loeve expansion 130—131
Chaotic signal analysis, higher order statistics, dimension estimation, rank dimensions, local intrinsic dimensionality 129—130 133—134
Chaotic signal analysis, higher order statistics, dimension estimation, rank dimensions, local intrinsic dimensionality, fourth-order 136—137
Chaotic signal analysis, higher order statistics, dimension estimation, rank dimensions, local intrinsic dimensionality, higher order 134—136
Chaotic signal analysis, higher order statistics, dimension estimation, rank dimensions, local intrinsic dimensionality, independent component analysis 137—142
Chaotic signal analysis, higher order statistics, dimension estimation, rank dimensions, reconstructed phase-space 131—133
Chaotic signal analysis, higher order statistics, embedding 117—126
Chaotic signal analysis, higher order statistics, embedding, delays 118—126
Chaotic signal analysis, higher order statistics, embedding, delays, method 118—119
Chaotic signal analysis, higher order statistics, embedding, delays, optimum choice 119—126
Chaotic signal analysis, higher order statistics, fourth-order extensions 134—142
Chaotic signal analysis, higher order statistics, introduction 105—106
Chinese remainder theorem, number theory 163—164
CHOS ESPRIT, narrowband signals 272—274
CHOS MUSIC, narrowband signals 269—272
Cruz — Banjanin algorithm frequency estimation 15—18
Cumulants, chaotic signal analysis 107—111
Cumulants, chaotic signal analysis, definitions 107
Cumulants, chaotic signal analysis, estimation issue 110—111
Cumulants, chaotic signal analysis, linear transformation 110
Cumulants, chaotic signal analysis, moments 108—110
Cumulants, chaotic signal analysis, properties 108
Cumulants, cyclic, sensor array processing 266—268
Discrete cosine transform 181—184
EKF, auxiliary training mode adaptation 387—389
EKF, MAPSD filter bank 390—391
Embedding, chaotic signal analysis 117—126
Embedding, chaotic signal analysis, delays 118—126
Embedding, chaotic signal analysis, delays, method 118—119
Factored state variable description 86
Factorization technique 187—188
Filters, floating point digital see "Roundoff noise floating
Filters, frequency sampling see "Frequency sampling filters fixed
FIR filters, roundoff noise 86 92—93
Fourier transform, discrete 156—158
Fraser's algorithm, chaotic signal analysis 122—124
Frequency estimation 1—79
Frequency estimation, Blackman — Tukey methods 2—3
Frequency estimation, linear prediction methods, approximate maximum likelihood estimation 9—10
Frequency estimation, linear prediction methods, auto-regressive models 13—14
Frequency estimation, linear prediction methods, auto-regressive models, auto-regressive moving-average models versus 14—15
Frequency estimation, linear prediction methods, auto-regressive moving-average models 3—15
Frequency estimation, linear prediction methods, autocorrelation method 6—7
Frequency estimation, linear prediction methods, Burg algorithm 11—13
Frequency estimation, linear prediction methods, covariance method 7—8
Frequency estimation, linear prediction methods, moving-average models 3 4
Frequency estimation, linear prediction methods, Prony method 5—6
Frequency estimation, linear prediction methods, Prony method, extended 8—9
Frequency estimation, periodograms 1—2
Frequency estimation, quotient-difference algorithm, Cruz — Banjanin algorithm, first 15—17
Frequency estimation, quotient-difference algorithm, Cruz — Banjanin algorithm, second 17—18
Frequency estimation, quotient-difference algorithm, error analysis 52—73
Frequency estimation, quotient-difference algorithm, error analysis, conclusion 74—75
Frequency estimation, quotient-difference algorithm, error analysis, effect of alpha 67—70
Frequency estimation, quotient-difference algorithm, error analysis, extremely simplified 52—55
Frequency estimation, quotient-difference algorithm, error analysis, Maple Code 75—77
Frequency estimation, quotient-difference algorithm, error analysis, Maple Code, two-sinusoid case 62—67
Frequency estimation, quotient-difference algorithm, error analysis, matrix inversion algorithm compared to 55—61
Frequency estimation, quotient-difference algorithm, error analysis, Stoica — Soderstrom algorithm compared to 70—73
Frequency estimation, quotient-difference algorithm, modified 29—30
Frequency estimation, quotient-difference algorithm, progressive 29
Frequency estimation, quotient-difference algorithm, simulated performance 30—51
Frequency estimation, quotient-difference algorithm, simulated performance, double sinusoid 42—46
Frequency estimation, quotient-difference algorithm, simulated performance, modified Todd — Cruz algorithm 35—41
Frequency estimation, quotient-difference algorithm, simulated performance, p = 3 case 47—51
Frequency estimation, quotient-difference algorithm, simulated performance, single sinusoids 31—34
Frequency estimation, quotient-difference algorithm, Stiefel theorems 20—28
Frequency estimation, quotient-difference algorithm, Todd — Cruz algorithm 18—20
Frequency estimation, quotient-difference algorithm, Todd — Cruz algorithm, modified 29—30
| Frequency sampling filters, fixed point roundoff effects 211—259
Frequency sampling filters, fixed point roundoff effects, conclusions 256
Frequency sampling filters, fixed point roundoff effects, finite word length effects 221—255
Frequency sampling filters, fixed point roundoff effects, finite word length effects, frequency sampling filters, type 1 228—237
Frequency sampling filters, fixed point roundoff effects, finite word length effects, frequency sampling filters, type 2 243—249
Frequency sampling filters, fixed point roundoff effects, finite word length effects, linear phase frequency sampling filters type 1 238—243
Frequency sampling filters, fixed point roundoff effects, finite word length effects, linear phase frequency sampling filters type 2 250—255
Frequency sampling filters, fixed point roundoff effects, frequency sampling filters, type 1 213—216
Frequency sampling filters, fixed point roundoff effects, frequency sampling filters, type 1, real impulse responses 213—214
Frequency sampling filters, fixed point roundoff effects, frequency sampling filters, type 1, real impulse responses, linear phase 214—216
Frequency sampling filters, fixed point roundoff effects, frequency sampling filters, type 2 216—212
Frequency sampling filters, fixed point roundoff effects, frequency sampling filters, type 2, real impulse responses 217—220
Frequency sampling filters, fixed point roundoff effects, frequency sampling filters, type 2, real impulse responses, linear phase 218—220
Frequency sampling filters, fixed point roundoff effects, introduction 211—212
Frequency sampling filters, fixed point roundoff effects, system functions, finite length coefficients 220—221
Fresnel transforms, number theory 184—186
Grassberger — Procaccia algorithm 126—129
Hartley theoretic transform 178—179
IIR filters, roundoff noise 86—87
Karhunen — Loeve expansion, chaotic signal analysis 130—131
Kullback divergence, chaotic signal analysis 113—114 119—121
Levinson algorithm 12
MAP symbol detection see "Blind adaptive MAP signal detection"
Maple Code, frequency estimation 75—77
MAPSD, algorithm, computer simulations 358—360 364
MAPSD, algorithm, decision feedback 374—376
MAPSD, algorithm, reduced complexity 365—382
MAPSD, algorithm, reduced complexity, LMS adaptation 366—372
MAPSD, algorithm, reduced complexity, LMS adaptation, fading channels 370—372
MAPSD, algorithm, time updates 398—400
MAPSD, filter bank, EKFs 390—391
MAPSD, optimal, known channels 347—349
MAPSD, suboptimal, decision feedback 372—380
Metric pruning, blind adaptive MAP symbol detection 380—382
Moving-average models, linear prediction methods 3 4
Neural networks, two-stage habituation based 301—337
Neural networks, two-stage habituation based, conclusions 330—331
Neural networks, two-stage habituation based, experimental results 317—330
Neural networks, two-stage habituation based, experimental results, Banzhaf sonograms 319—323
Neural networks, two-stage habituation based, experimental results, basic issues, spatio-temporal classification 317—319
Neural networks, two-stage habituation based, experimental results, classification, uncorrelated sequences 323—324
Neural networks, two-stage habituation based, experimental results, comparison, different training methods 325—328
Neural networks, two-stage habituation based, experimental results, hybrid habituated TDNN network 328
Neural networks, two-stage habituation based, experimental results, multiple habituation 328
Neural networks, two-stage habituation based, experimental results, principal component analysis 329
Neural networks, two-stage habituation based, experimental results, varying habituation parameters 329—330
Neural networks, two-stage habituation based, habituation based neural classifier 309—317
Neural networks, two-stage habituation based, habituation based neural classifier, general design structure 310—311
Neural networks, two-stage habituation based, habituation based neural classifier, other related structures 315—317
Neural networks, two-stage habituation based, habituation based neural classifier, theoretical properties 311—315
Neural networks, two-stage habituation based, introduction 301—303
Neural networks, two-stage habituation based, related biological mechanisms 303—309
Neural networks, two-stage habituation based, related biological mechanisms, classical conditioning 307—308
Neural networks, two-stage habituation based, related biological mechanisms, habituation 304—306
Neural networks, two-stage habituation based, related biological mechanisms, sensitization 307
Neural networks, two-stage habituation based, related biological mechanisms, temporal information encoding 308—309
Number theory, two-dimensional transforms 155—210
Number theory, two-dimensional transforms, classes, two-dimensional 170—173
Number theory, two-dimensional transforms, conclusions 204—205
Number theory, two-dimensional transforms, implementation, VLSI 190—204
Number theory, two-dimensional transforms, implementation, VLSI, Fermat ALU 195—203
Number theory, two-dimensional transforms, implementation, VLSI, Fermat ALU, CRT conversion 197—198
Number theory, two-dimensional transforms, implementation, VLSI, Fermat design 203—204
Number theory, two-dimensional transforms, implementation, VLSI, Fermat field column compression multiplier 190—195
Number theory, two-dimensional transforms, introduction 156—159
Number theory, two-dimensional transforms, mathematical preliminaries 159—163
Number theory, two-dimensional transforms, mathematical preliminaries, fields 160—161
Number theory, two-dimensional transforms, mathematical preliminaries, rings 160 162
Number theory, two-dimensional transforms, multiplexed processors 187—190
Number theory, two-dimensional transforms, multiplexed processors, two-dimensional factorization technique 187—188
Number theory, two-dimensional transforms, number theoretic transforms 167—173
Number theory, two-dimensional transforms, number theoretic transforms, classes 173—186
Number theory, two-dimensional transforms, number theoretic transforms, classes, implementation 178—186
Number theory, two-dimensional transforms, number theoretic transforms, classes, implementation, discrete cosine transforms over finite fields 181—184
Number theory, two-dimensional transforms, number theoretic transforms, classes, implementation, one-dimensional and two-dimensional Hartley number theoretic transform 178—180
Number theory, two-dimensional transforms, number theoretic transforms, classes, implementation, two-dimensional Fresnel transforms 184—186
Number theory, two-dimensional transforms, number theoretic transforms, classes, multi-dimensional mapping 173—178
Number theory, two-dimensional transforms, number theoretic transforms, one-dimensional 168—170
Number theory, two-dimensional transforms, number theoretic transforms, two-dimensional 170—173
Number theory, two-dimensional transforms, residue number systems 163—167
Number theory, two-dimensional transforms, residue number systems, Chinese remainder theorem 163—164
Number theory, two-dimensional transforms, residue number systems, modulus replication 165—167
Number theory, two-dimensional transforms, residue number systems, quadratic residue number system 165
Predictor filter, auto-regressive models 4—5
Prony method 5—6
Prony method, extended 5—9
Rossler system, chaotic signal analysis 113—114 119 133—134 146
Roundoff noise, floating point digital filters 79—103
Roundoff noise, floating point digital filters, basic error models 80—86
Roundoff noise, floating point digital filters, coefficient sensitivity 97—99
Roundoff noise, floating point digital filters, deterministic signals 91—92
Roundoff noise, floating point digital filters, direct form II filter 93—95
Roundoff noise, floating point digital filters, example 99—100
Roundoff noise, floating point digital filters, example, cascade structure 100
Roundoff noise, floating point digital filters, example, direct form II 100
Roundoff noise, floating point digital filters, FIR filters 86 92—93
Roundoff noise, floating point digital filters, high level error model 87—88
Roundoff noise, floating point digital filters, HR filters 86—87
Roundoff noise, floating point digital filters, inner product 86—87
Roundoff noise, floating point digital filters, inner product, signal flow graphs 96—97
Roundoff noise, floating point digital filters, introduction 79—80
Roundoff noise, floating point digital filters, state space filters 87
Roundoff noise, floating point digital filters, statistics of errors 89—90
Roundoff noise, floating point digital filters, summary 95—96
Roundoff noise, floating point digital filters, typical M matrices 90—91
Sensor array processing 259—300
Sensor array processing, cyclic cumulants 266—268
Sensor array processing, discussion 284—285
Sensor array processing, introduction 259—266
Sensor array processing, introduction, literature review 263—266
Sensor array processing, introduction, signal model 259—263
Sensor array processing, localization with nonstationary antennas 278—284
Sensor array processing, localization with nonstationary antennas, beamformer for coherent signals 281—282
Sensor array processing, localization with nonstationary antennas, direction finding using moving array 279—281
Sensor array processing, localization with nonstationary antennas, simulation examples 282—284
Sensor array processing, signal selective direction finding 268—278
Sensor array processing, signal selective direction finding, bandwidth independent localization algorithms 274—276
Sensor array processing, signal selective direction finding, CHOS ESPRIT for narrowband signals 272—274
Sensor array processing, signal selective direction finding, CHOS MUSIC for narrowband signals 269—272
Sensor array processing, signal selective direction finding, simulation examples 276—278
Shannon differential entropy function, chaotic signal analysis 120—121
Sinusoids see "Frequency estimation"
State space filters, roundoff noise 87
Stiefel theorems, frequency estimation 20—28
Stoica — Soderstrom algorithm, frequency estimation 70—73
TDMA mobile radio, blind adaptive MAP symbol detection see "Blind adaptive MAP symbol detection"
TDMA signal algorithm, dual-mode algorithm, blind adaptive MAP symbol detection 382—396
Todd — Cruz algorithm, frequency estimation 18—20 35—41
Transforms, number theory see "Number theory two-dimensional
Viterbi algorithm, blind 358 360
White Gaussian noise, auto-regressive models 4—5
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