Авторизация
Поиск по указателям
Kay S.M. — Fundamentals of statistical signal processing, volume 1: estimation theory
Обсудите книгу на научном форуме Нашли опечатку?
Название: Fundamentals of statistical signal processing, volume 1: estimation theoryАвтор: Kay S.M. Аннотация: This text is geared towards a one-semester graduate-level course in statistical signal processing and estimation theory. The author balances technical detail with practical and implementation issues, delivering an exposition that is both theoretically rigorous and application-oriented. The book covers topics such as minimum variance unbiased estimators, the Cramer-Rao bound, best linear unbiased estimators, maximum likelihood estimation, recursive least squares, Bayesian estimation techniques, and the Wiener and Kalman filters. The author provides numerous examples, which illustrate both theory and applications for problems such as high-resolution spectral analysis, system identification, digital filter design, adaptive beamforming and noise cancellation, and tracking and localization. The primary audience will be those involved in the design and implementation of optimal estimation algorithms on digital computers. The text assumes that you have a background in probability and random processes and linear and matrix algebra and exposure to basic signal processing. Students as well as researchers and practicing engineers will find the text an invaluable introduction and resource for scalar and vector parameter estimation theory and a convenient reference for the design of successive parameter estimation algorithms.
Язык: Рубрика: Математика /Вероятность /Статистика и приложения /Статус предметного указателя: Готов указатель с номерами страниц ed2k: ed2k stats Год издания: 1993Количество страниц: 595Добавлена в каталог: 04.06.2005Операции: Положить на полку |
Скопировать ссылку для форума | Скопировать ID
Предметный указатель
Maximum likelihood estimator, complex data 530—31 563—65 Maximum likelihood estimator, definition 162 182 Maximum likelihood estimator, efficiency 164 187 Maximum likelihood estimator, Gaussian PDF 185 Maximum likelihood estimator, invariance 174—76 185 Maximum likelihood estimator, numerical determination 177—82 187—89 Maximum likelihood estimator, probability density function, asymptotic 167 183 211—13 Maximum likelihood estimator, properties, asymptotic 172 201—2 Mean square bandwidth 55 Mean square error matrix 361—62 390 Mean square error, Bayesian 311 320 347 533 Mean square error, classical 19 Minimal sufficient statistic 102 117 Minimum mean square error estimator, Bayesian, classical 19 311 Minimum mean square error estimator, Bayesian, definition 313 316 346 Minimum mean square error estimator, Bayesian, performance 360 364—65 534 Minimum mean square error estimator, Bayesian, properties 349—50 Minimum variance distortionless response 546 Minimum variance unbiased estimator, definition 20 Minimum variance unbiased estimator, determination of 109 112—13 Minimum variance unbiased estimator, linear model 85—86 MLE (see Maximum likelihood estimator) MMSE (see Minimum mean square error estimator) Modeling (see also Autoregressive and Linear predictive coding) Modeling, dynamical signal 421 Modeling, identifiability 85 Modeling, least squares 232—34 Modeling, linearization 143 259 273 451 461 Modeling, speech spectrum 5 Moments, method of definition 293 Moments, method of exponential parameter, estimator 292 295—97 Moments, method of Gaussian mixture 290—91 293—94 Monte Carlo method 10 164—167 205—10 Moving average, asymptotic MLE 190—91 Moving average, definition 580 MSE (see Mean square error) MVU (see Minimum variance unbiased estimator) Narrowband representation 495 Newton — Raphson iteration 179—82 187 259 Neyman — Fisher factorization 104—5 117 127—29 Normal equations 225 387 Notational conventions 13 (see also Appendix 2) Nuisance parameters 329 Observation equation 446 Observation matrix 84 100 140 224 Order statistics 114 Orthogonality 89 385 orthogonal) Outliers 170 PDF (see Probability density functions) Periodogram 80 190 195 197 204 Phase-locked loop 273—75 Posterior PDF, Bayesian linear model 326 533 Posterior PDF, definition 313 317 Power estimation, random process 66 203 553—54 Power spectral density 576—77 Prediction, Kalman 440—41 469—70 Prediction, Wiener 400 Prior PDF, conjugate 335 (see also Reproducing PDF) Prior PDF, definition 313 Prior PDF, noninformative 332 336 Probability density functions, chi-squared 122 575 Probability density functions, complex Gaussian, conditional 508—9 562 Probability density functions, complex Gaussian, definition 503—4 507 Probability density functions, complex Gaussian, exponential 122 Probability density functions, complex Gaussian, exponential family 110 124 Probability density functions, complex Gaussian, gamma, inverted 329—30 355 Probability density functions, complex Gaussian, properties 508—9 550 558—62 Probability density functions, Gaussian 574 Probability density functions, Gaussian mixture 150 Probability density functions, Gaussian, conditional 323—25 337—39 Probability density functions, Laplacian 63 Probability density functions, lognormal 147 Probability density functions, Rayleigh 122 371 Processing gain 554 Projection theorem, orthogonal 228—29 386 Pronv method 264 PSD (see Power spectral density) Pseudorandom noise 92 165 206 Pythagorean theorem, least squares 276 Quadratic form, definition 568 Quadratic form, moments 76 Quadrature signal 495—96 Radar signal processing 1 Random number generator (see Pseudorandom noise) Random variable, complex 500—501 Range estimation 1 14 53—56 192 Rao-Blackwell-Lehmann-Scheffe theorem 22 109 118—19 130—31 Rayleigh fading 347 RBLs (see Rao-Blackwell-Lehmann-Scheffe theorem) Regression, nonlinear 254 Regularity conditions 30 44 63 67 70 Reproducing PDF 321 334—35 Ricatti equation 443 Risk, Bayes 342 Sample mean estimator 115 121 164 Sample variance estimator 121 164 Scoring 180 187 Seismic signal processing 365 Separability, least squares 222—23 256 Signal amplitude estimator 136 498—500 Sinusoidal estimation, amplitudes 88—90 Sinusoidal estimation, complex data 525—27 531—32 534—35 543 Sinusoidal estimation, CRLB for frequency 36 Sinusoidal estimation, CRLB for parameters 56—57 542 Sinusoidal estimation, CRLB for phase 33 Sinusoidal estimation, EM for frequency 187—89 Sinusoidal estimation, least squares for parameters 255—56 Sinusoidal estimation, method of moments for frequency 300 306 Sinusoidal estimation, MLE for parameters 193—95 203-4 Sinusoidal estimation, phase estimator 123 167—72 Sinusoidal estimation, sufficient statistics 117—18 Sinusoidal modeling, complex 496 Slutsky’s theorem 201 Smoothing, Wiener 400 Sonar signal processing 2 Spatial frequency 58 195 Spectral estimation, autoregressive 60 Spectral estimation, Fourier analysis 88—90 Spectral estimation, periodogram 204 538—39 543 552 Speech recognition 4 State transition matrix 426 State vector 424 Statistical linearization 39 200 Sufficient statistic 22 102—3 107 116 System identification, nonrandom FIR 90—94 99 System identification, random FIR 452—55 Tapped delay line (see FIR) Threshold effect 170 Time delay estimation 53—56 142—46 Time difference of arrival 142 Time series 6 Tracking, frequency 470 (see also Phase-locked loop) Tracking, vehicle position 456—66 Unbiased estimator 16 22 Vector spaces, least squares 227—30 Vector spaces, random variables 384 Wavenumber (see Spatial frequency) WGN (see White Gaussian noise) White Gaussian noise, complex 517 White Gaussian noise, real 7 White noise 576 Whitening, Kalman 441 444 Whitening, matrix transformation 94—96 Wide sense stationary 575 Wiener filtering 365—70 373—74 379 400-409 443 Wiener-Hopf equations, filtering 403 Wiener-Hopf equations, prediction 406—7 WSS (see Wide sense stationary) Yule-Walker equations, AR 198 579 Yule-Walker equations, ARMA 267
Реклама
|
|
О проекте