Statistical and adaptive signal processing: spectral estimation, signal modeling, adaptive filtering, and array processing
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boston
Artech House
2005
|
| Series: | Artech House signal processing library
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Originally published: Boston : McGraw-Hill, c2000. Includes bibliographical references (p. 769-785) and index |
| Physical Description: | xviii, 796 p. ill. 26 cm |
| ISBN: | 1580536107 9781580536103 |
Staff View
MARC
| LEADER | 00000nam a2200000zc 4500 | ||
|---|---|---|---|
| 001 | BV035447256 | ||
| 003 | DE-604 | ||
| 005 | 20190605 | ||
| 007 | t | ||
| 008 | 090422s2005 xxua||| |||| 00||| eng d | ||
| 010 | |a 2005045267 | ||
| 020 | |a 1580536107 |c alk. paper |9 1-580-53610-7 | ||
| 020 | |a 9781580536103 |9 978-1-580-53610-3 | ||
| 035 | |a (OCoLC)58791091 | ||
| 035 | |a (DE-599)BVBBV035447256 | ||
| 040 | |a DE-604 |b ger |e aacr | ||
| 041 | 0 | |a eng | |
| 044 | |a xxu |c US | ||
| 049 | |a DE-Aug4 |a DE-83 |a DE-739 | ||
| 050 | 0 | |a TK5102.9 | |
| 082 | 0 | |a 621.382/2 |2 22 | |
| 084 | |a ZN 6025 |0 (DE-625)157494: |2 rvk | ||
| 100 | 1 | |a Manolakis, Dimitris G. |e Verfasser |0 (DE-588)1050810430 |4 aut | |
| 245 | 1 | 0 | |a Statistical and adaptive signal processing |b spectral estimation, signal modeling, adaptive filtering, and array processing |c Dimitris G. Manolakis, Vinay K. Ingle, Stephen M. Kogon |
| 264 | 1 | |a Boston |b Artech House |c 2005 | |
| 300 | |a xviii, 796 p. |b ill. |c 26 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Artech House signal processing library | |
| 500 | |a Originally published: Boston : McGraw-Hill, c2000. | ||
| 500 | |a Includes bibliographical references (p. 769-785) and index | ||
| 650 | 4 | |a Signal processing |x Statistical methods | |
| 650 | 4 | |a Adaptive signal processing | |
| 650 | 0 | 7 | |a Signalverarbeitung |0 (DE-588)4054947-1 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Stochastisches Signal |0 (DE-588)4140374-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Adaptive Signalverarbeitung |0 (DE-588)4128146-9 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Signalverarbeitung |0 (DE-588)4054947-1 |D s |
| 689 | 0 | 1 | |a Stochastisches Signal |0 (DE-588)4140374-5 |D s |
| 689 | 0 | 2 | |a Adaptive Signalverarbeitung |0 (DE-588)4128146-9 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Ingle, Vinay K. |e Sonstige |4 oth | |
| 700 | 1 | |a Kogon, Stephen M. |e Sonstige |4 oth | |
| 856 | 4 | 2 | |m Digitalisierung UB Passau - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017367382&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-017367382 | ||
Record in the Search Index
| _version_ | 1804138896621043712 |
|---|---|
| adam_text | CONTENTS Preface xvii 1 Introduction 1 1.1 Random Signals 1.2 Spectral Estimation 1 8 1.3 Signal Modeling 11 1.3.1 Rational or Pole-Zero Models / 1.3.2 Fractional Pole-Zero Models and Fractal Models 1.4 Adaptive Filtering 16 1.4.1 Applications ofAdaptive Filters / 1.4.2 Features of Adaptive Filters 1.5 Array Processing 2 Fundamentals of DiscreteTime Signal Processing 2.1 Discrete-Time Signals 25 29 47 2.3.1 Analysis of Linear, Time-Invariant Systems / 2.3.2 Response to Periodic Inputs / 2.3.3 Correlation Analysis and Spectral Density 2.4 Minimum-Phase and System Invertibility 54 33 33 37 2.2.1 Fourier Transforms and Fourier Series / 2.2.2 Sampling of Continuous-Time Signals / 2.2.3 The Discrete Fourier Transform /2.2.4 The Z-Transform / 2.2.5 Representation of Narrowband Signals 64 Problems 70 75 75 3.1.1 Distribution and Density Functions / 3.1.2 Statistical Averages /3.1.3 Some Useful Random Variables 125 3.5.1 Transformations Using Eigen-decomposition / 3.5.2 Transformations Using Triangular Decomposition / 3.5.3 The Discrete KarhunenLoève Transform 83 97 133 3.7 Summary 142 Problems 143 4 Linear SignalModels 149 149 4.1.1 Linear Nonparametric Signal Models / 4.1.2 Parametric Pole-Zero Signal Models / 4.1.3 Mixed Processes and the Wold Decomposition 4.2 All-Pole Models 156 4.2.1 Model Properties / 4.2.2 All-Pole Modeling and Linear Prediction / 4.2.3 Autoregressive Models / 4.2.4 Lower-Order Models 4.3 All-Zero Models 4.3.1 Model Properties / 4.3.2 Moving-Average Models / 4.3.3 Lower-Order Models „ 177 4.4.1 Model Properties / 4.4.2 Autoregressive Moving-Average Models /
4.4.3 The First-Order Pole-Zero Model 1: PZ (1,1) / 4.4.4 Summary and Dualities on the Unit Circle 172 182 4.6 Cepstrum of Pole-Zero Models 184 4.6.1 Pole-Zero Models / 4.6.2 All-Pole Models / 4.6.3 All-Zero Models 4.7 Summary 189 Problems 189 5.1 Spectral Analysis of Deterministic Signals 195 196 5.1.1 Effect of Signal Sampling / 5.1.2 Windowing, Periodic Extension, and Extrapolation / 5.1.3 Effect of Spectrum Sampling / 5.1.4 Effects of Windowing: Leakage and Loss of Resolution / 5.1.5 Summary 5.2 Estimation of the Autocorrelation of Stationary Random Signals 209 5.3 Estimation of the Power Spectrum of Stationary Random Signals xi Contents 4.5 Models with Poles 5 Nonparametric Power Spectrum Estimation 3.6.1 Properties of Estimators / 3.6.2 Estimation of Mean / 3.6.3 Estimation of Variance 4.1 Introduction 3.2.1 Definitions and Second-Order Moments / 3.2.2 Linear Transformations of Random Vectors / 3.2.3 Normal Random Vectors / 3.2.4 Sums of Independent Random Variables 3.3.1 Description Using Probability Functions / 3.3.2 Second-Order Statistical Description / 3.3.3 Stationarity / 3.4.1 Time-Domain Analysis / 3.4.2 Frequency-Domain Analysis / 3.4.3 Random Signal Memory / 3.4.4 General Correlation Matrices / 3.4.5 Correlation Matrices from Random Processes Theory 70 3.3 Discrete-Time Stochastic Processes 115 3.6 Principles of Estimation 2.6 Summary 3.2 Random Vectors Stationary Random Inputs Representation 2.5.1 All-Zero Lattice Structures / 2.5.2 All-Pole Lattice Structures 3 Random Variables, Vectors, and Sequences 3.4 Linear Systems with 3.5 Whitening and Innovations
2.4.1 System Invertibility and Minimum-Phase Systems / 2.4.2 All-Pass Systems / 2.4.3 Minimum-Phase and All-Pass Decomposition / 2.4.4 Spectral Factorization 3.1 Random Variables 2.1.1 Continuous-Time, DiscreteTime, and Digital Signals / 2.1.2 Mathematical Description of Signals / 2.1.3 Real-World Signals 2.2 Transform-Domain Representation of Deterministic Signals 2.3 Discrete-Time Systems 2.5 Lattice Filter Realizations 1.5.1 Spatial Filtering or Beamforming /1.5.2 Adaptive Interference Mitigation in Radar Systems /1.5.3 Adaptive Sidelobe Canceler 1.6 Organization of the Book 4.4 Pole-Zero Models 3.3.4 Ergodicity / 3.3.5 Random Signal Variability / 3.3.6 Frequency-Domain Description of Stationary Processes 212 5.3.1 Power Spectrum Estimation Using the Periodogram / 5.3.2 Power Spectrum Estimation by Smoothing a Single Periodogram— The Blackman-Tukey Method / 5.3.3 Power Spectrum Estimation by Averaging Multiple Periodograms—The WelchBartlett Method / 5.3.4 Some Practical Considerations and Examples
xii Contents 5.4 Joint Signal Analysis 237 5.4.1 Estimation of Cross-Power Spectrum / 5.4.2 Estimation of Frequency Response Functions 5.5 Multitaper Power Spectrum Estimation 6.7 Inverse Filtering and Deconvolution 246 5.6 Summary 254 Problems 255 261 6.1 Optimum Signal Estimation 261 6.2 Linear Mean Square Error Estimation 264 6.2.1 Error Performance Surface / 6.2.2 Derivation of the Linear MMSE Estimator / 6.2.3 PrincipalComponent Analysis of the Optimum Linear Estimator / 6.2.4 Geometric Interpretations and the Principle of Orthogonality / 6.2.5 Summary and Further Properties 6.3 Solution of the Normal Equations 274 6.4 Optimum Finite Impulse Response Filters 278 6.4.1 Design and Properties / 6.4.2 Optimum FIR Filters for Stationary Processes / 6.4.3 Frequency-Domain Interpretations 6.5 Linear Prediction 286 6.5.1 Linear Signal Estimation / 6.5.2 Forward Linear Prediction / 6.5.3 Backward Linear Prediction / 6.5.4 Stationary Processes / 6.5.5 Properties 6.6 Optimum Infinite Impulse Response Filters 306 8.4 Linear Least-Squares Lattice-Ladder Structure / 7.3.3 Simplifications for Stationary Stochastic Processes / 7.3.4 Algorithms Based on the UDUH Signal Estimation Decomposition 5.5.1 Estimation ofAuto Power Spectrum / 5.5.2 Estimation of Cross Power Spectrum Optimum Linear Filters Linear Prediction Using the Infinite Past-Whitening 295 6.6.1 Noncausal IIR Filters / 6.6.2 Causal IIR Filters / 6.6.3 Filtering ofAdditive Noise / 6.6.4 6.8 Channel Equalization in Data Transmission Systems 310 6.8.1 Nyquist’s Criterion for Zero ISI / 6.8.2 Equivalent Discrete-Ћте Channel
Model / 6.8.3 Linear Equalizers / 6.8.4 Zero-Forcing Equalizers / 6.8.5 Minimum MSE Equalizers 6.9 Matched Filters and Eigenfilters 319 6.9.1 Deterministic Signal in Noise / 6.9.2 Random Signal in Noise 6.10 Summary 325 Problems 325 7.4 Algorithms of Levinson and Levinson-Durbin 355 7.5 Lattice Structures for Optimum FIR Filters and Predictors 361 368 7.6.1 Direct Schür Algorithm / 7.6.2 Implementation Considerations / 7.6.3 Inverse Schür Algorithm 7.1 Fundamentals of OrderRecursive Algorithms 334 7.1.1 Matrix Partitioning and Optimum Nesting / 7.1.2 Inversion of Partitioned Hermitian Matrices / 7.1.3 Levinson Recursion for the Optimum Estimator / 7.1.4 OrderRecursive Computation of the LDLfi Decomposition / 7.1.5 OrderRecursive Computation of the Optimum Estimate 7.2 Interpretations of Algorithmic Quantities 343 7.2.1 Innovations and Backward Prediction / 7.2.2 Partial Correlation / 7.2.3 Order Decomposition of the Optimum Estimate / 7.2.4 Gram-Schmidt Orthogonalization 7.3 Order-Recursive Algorithms for Optimum FIR Filters 347 7.3.1 Order-Recursive Computation of the Optimum Filter / 7.3.2 of Toeplitz Matrices 374 7.7.1 LDLHDecomposition of Inverse of a Toeplitz Matrix / 7.7.2 LDLH Decomposition of a Toeplitz Matrix / 7.7.3 Inversion of Real Toeplitz Matrices 7.8 Kalman Filter Algorithm 378 7.9 Summary 387 Problems 389 395 8.1 The Principle of Least Squares 395 8.2 Linear Least-Squares Error Estimation 396 8.2.1 Derivation of the Normal Equations / 8.2.2 Statistical Properties of Least-Squares Estimators 8.3 Least-Squares FIR Filters 8.6 LS Computations Using
Orthogonalization Techniques 422 8.7 LS Computations Using the Singular Value Decomposition 431 8.7.1 Singular Value Decomposition / 8.7.2 Solution of the LS Problem / 8.7.3 Rank-Deficient LS Problems 7.8.1 Preliminary Development / 7.8.2 Development of Kalman Filter 8 Least-Squares Filtering and Prediction 8.5 LS Computations Using the Normal Equations 416 8.6.1 Householder Reflections / 8.6.2 The Givens Rotations / 8.6.3 Gram-Schmidt Orthogonalization 7.7 Triangularization and Inversion 7 Algorithms and Structures for Optimum Linear Filters 333 8.4.1 Signal Estimation and Linear Prediction / 8.4.2 Combined Forward and Backward Linear Prediction (FBLP) / 8.4.3 Narrowband Interference Cancelation 8.5.1 Linear LSE Estimation / 8.5.2 LSE FIR Filtering and Prediction 7.5.1 Lattice-Ladder Structures / 7.5.2 Some Properties and Interpretations / 7.5.3 Parameter Conversions 7.6 Algorithm of Schür xiii 411 8.8 Summary 438 Problems 439 Signal Modeling and Parametric Spectral Estimation 445 9.1 The Modeling Process: Theory and Practice 445 9.2 Estimation of All-Pole Models 449 9.2.1 Direct Structures / 9.2.2 Lattice Structures / 9.2.3 Maximum Entropy Method / 9.2.4 Excitations with Line Spectra 9.3 Estimation of Pole-Zero Models 406 9.3.1 Known Excitation / 9.3.2 Unknown Excitation / 9.3.3 462 Contents
xiv Contents Nonlinear Least-Squares Optimization 9.4 Applicatons 10.5 Recursive Least-Squares Adaptive Filters 548 467 9.4.1 Spectral Estimation / 9.4.2 Speech Modeling 9.5 Minimum-Variance Spectrum Estimation 9.6 Harmonic Modelsand Frequency Estimation Techniques 471 478 9.6.1 Harmonic Model / 9.6.2 Pisarenko Harmonic Decomposition / 9.6.3 MUSIC Algorithm / 9.6.4 Minimum-Norm Method / 9.6.5 ESPRIT Algorithm 9.7 Summary 493 Problems 494 10 Adaptive Filters 499 10.1 Typical Applications of Adaptive Filters 500 10.1.1 Echo Cancelation in Communications / 10.1.2 Equalization of Data Communications Channels / 10.1.3 Linear Predictive Coding / 10.1.4 Noise Cancelation 10.2 Principles of Adaptive Filters 506 10.2.1 Features ofAdaptive Filters / 10.2.2 Optimum versus Adaptive Filters / 10.2.3 Stability and Steady-State Performance of Adaptive Filters / 10.2.4 Some Practical Considerations 10.3 Method of Steepest Descent 10.4 Least-Mean-Square Adaptive Filters 516 524 10.4.1 Derivation / 10.4.2 Adaptation in a Stationary SOE / 10.4.3 Summary and Design Guidelines / 10.4.4 Applications of the LMS Algorithm / 10.4.5 Some Practical Considerations 10.5.1 LS Adaptive Filters / 10.5.2 Conventional Recursive Least-Squares Algorithm / 10.5.3 Some Practical Considerations / 10.5.4 Convergence and Performance Analysis 10.6 RLS Algorithms for Array Processing 560 10.6.1 LS Computations Using the Cholesky and QR Decompositions / 10.6.2 Two Useful Lemmas / 10.6.3 The QR-RLS Algorithm / 10.6.4 Extended QR-RLS Algorithm / 10.6.5 The Inverse QR-RLS Algorithm / 10.6.6 Implementation of QR-RLS
Algorithm Using the Givens Rotations / 10.6.7Implementation of Inverse QR-RLS Algorithm Using the Givens Rotations / 10.6.8 Classification of RLS Algorithms for Array Processing 10.7 Fast RLS Algorithms for FIR Filtering 573 10.7.1 Fast Fixed-Order RLS FIR Filters / 10.7.2 RLS LatticeLadder Filters / 10.7.3 RLS Lattice-Ladder Filters Using Error Feedback Updatings / 10.7.4 Givens Rotation-Based LS LatticeLadder Algorithms / 10.7.5 Classification of RLS Algorithms for FIR Filtering 10.8 Tracking Performance of Adaptive Algorithms 11 Array Processing 11.1 Array Fundamentals 10.8.1 Approaches for Nonstationary SOE / 10.8.2 Preliminaries in Performance Analysis / 10.8.3 The LMS Algorithm / 10.8.4 The RLS Algorithm with Exponential Forgetting / 10.8.5 Comparison of Tracking Performance 10.9 Summary 607 Problems 608 11.8 Space-Time Adaptive Processing 683 622 11.9 Summary 685 Problems 686 11.1.1 Spatial Signals / 11.1.2 Modulation-Demodulation / 11.1.3 Array Sigruil Model / 11.1.4 The Sensor Array: Spaimi Sampling 11.2 Conventional Spatial Filtering: Beamforming 631 11.2.1 Spatial Matched Filter / 11.2.2 Tapered Beamforming 11.3 Optimum Array Processing 641 11.3.1 Optimum Beamforming / 11.3.2 Eigenanalysis of the Optimum Beamformer / 11.3.3 Interference Cancelation Performance / 11.3.4 Tapered Optimum Beamforming / 11.3.5 The Generalized Sidelobe Canceler 11.4 Performance Considerations for Optimum Beamformers 652 11.4.1 Effect of Signal Mismatch / 11.4.2 Effect of Bandwidth 11.5 Adaptive Beamforming 659 11.5.1 Sample Matrix Inversion / 11.5.2 Diagonal Loading with the SMI
Beamformer / 11.5.3 Implementation of the SMI Beamformer / 11.5.4 Sample-bySample Adaptive Methods 11.6 Other Adaptive Array Processing Methods 590 621 671 11.6.1 Linearly Constrained Minimum-Variance Beamformers / 11.6.2 Partially Adaptive Arrays / 11.6.3 Sidelobe Cancelers 11.7 Angle Estimation 11.7.1 Maximum-Likelihood Angle Estimation / 11.7.2 Cramér-Rao Lower Bound on Angle Accuracy / 11.7.3 Beamsplitting Algorithms / 11.7.4 Model-Based Methods 678 12 Further Topics 12.1 Higher-Order Statistics in Signal Processing XV 691 691 12.1.1 Moments, Cumulants, and Polyspectra / 12.1.2 HigherOrder Moments and LT1 Systems / 12.1.3 Higher-Order Moments of Linear Signal Models 12.2 Blind Deconvolution 697 12.3 Unsupervised Adaptive Filters—Blind Equalizers 702 12.3.1 Blind Equalization / 12.3.2 Symbol Rate Blind Equalizers / 12.3.3 ConstantModulus Algorithm 12.4 Fractionally Spaced Equalizers 709 12.4.1 Zero-Forcing Fractionally Spaced Equalizers / 12.4.2 MMSE Fractionally Spaced Equalizers / 12.4.3 Blind Fractionally Spaced Equalizers 12.5 Fractional Pole-Zero Signal Models 716 12.5.1 Fractional Unit-Pole Model / 12.5.2 Fractional PoleZero Models: FPZ (p, d, q) / 12.5.3 Symmetric a-Stable Fractional Pole-Zero Processes 12.6 Self-Similar Random Signal Models 725 12.6.1 Self-Similar Stochastic Processes / 12.6.2 Fractional Brownian Motion / 12.6.3 Fractional Gaussian Noise / 12.6.4 Simulation of Fractional Brownian Motions and Fractional Gaussian Noises / 12.6.5 Estimation of Long Memory / Contents
12.6.6 Fractional Lévy Stable Motion xvi Contents D.2 Matrices 12.7 Summary 741 Problems 742 D.3 Determinant of a Square Matrix Appendix A Matrix Inversion Lemma Appendix В Gradients and Optimization in Complex Space D.4 Unitary Matrices 747 B.2 Lagrange Multipliers 749 762 D.4.1 Hermitian Forms after Unitary Transformations / D.4.2 Significant Integral of Quadratic and Hermitian Forms 747 Gradient 760 D.3.1 Properties of the Determinant / D.3.2 Condition Number 745 B.l 756 D.2.1 Some Definitions / D.2.2 Properties of Square Matrices D.5 Positive Definite Matrices 764 Appendix C Matlab Functions Appendix D Useful Results from Matrix Algebra D.l Complex-Valued Vector Space Some Definitions 753 Appendix Б Minimum Phase Test for Polynomials 767 755 Bibliography 769 755 Index 787
|
| any_adam_object | 1 |
| author | Manolakis, Dimitris G. |
| author_GND | (DE-588)1050810430 |
| author_facet | Manolakis, Dimitris G. |
| author_role | aut |
| author_sort | Manolakis, Dimitris G. |
| author_variant | d g m dg dgm |
| building | Verbundindex |
| bvnumber | BV035447256 |
| callnumber-first | T - Technology |
| callnumber-label | TK5102 |
| callnumber-raw | TK5102.9 |
| callnumber-search | TK5102.9 |
| callnumber-sort | TK 45102.9 |
| callnumber-subject | TK - Electrical and Nuclear Engineering |
| classification_rvk | ZN 6025 |
| ctrlnum | (OCoLC)58791091 (DE-599)BVBBV035447256 |
| dewey-full | 621.382/2 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 621 - Applied physics |
| dewey-raw | 621.382/2 |
| dewey-search | 621.382/2 |
| dewey-sort | 3621.382 12 |
| dewey-tens | 620 - Engineering and allied operations |
| discipline | Elektrotechnik / Elektronik / Nachrichtentechnik |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02181nam a2200505zc 4500</leader><controlfield tag="001">BV035447256</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20190605 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">090422s2005 xxua||| |||| 00||| eng d</controlfield><datafield tag="010" ind1=" " ind2=" "><subfield code="a">2005045267</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1580536107</subfield><subfield code="c">alk. paper</subfield><subfield code="9">1-580-53610-7</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781580536103</subfield><subfield code="9">978-1-580-53610-3</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)58791091</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV035447256</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">aacr</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="a">xxu</subfield><subfield code="c">US</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-Aug4</subfield><subfield code="a">DE-83</subfield><subfield code="a">DE-739</subfield></datafield><datafield tag="050" ind1=" " ind2="0"><subfield code="a">TK5102.9</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">621.382/2</subfield><subfield code="2">22</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ZN 6025</subfield><subfield code="0">(DE-625)157494:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Manolakis, Dimitris G.</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1050810430</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Statistical and adaptive signal processing</subfield><subfield code="b">spectral estimation, signal modeling, adaptive filtering, and array processing</subfield><subfield code="c">Dimitris G. Manolakis, Vinay K. Ingle, Stephen M. Kogon</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Boston</subfield><subfield code="b">Artech House</subfield><subfield code="c">2005</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">xviii, 796 p.</subfield><subfield code="b">ill.</subfield><subfield code="c">26 cm</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Artech House signal processing library</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Originally published: Boston : McGraw-Hill, c2000.</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">Includes bibliographical references (p. 769-785) and index</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Signal processing</subfield><subfield code="x">Statistical methods</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Adaptive signal processing</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Signalverarbeitung</subfield><subfield code="0">(DE-588)4054947-1</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Stochastisches Signal</subfield><subfield code="0">(DE-588)4140374-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Adaptive Signalverarbeitung</subfield><subfield code="0">(DE-588)4128146-9</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Signalverarbeitung</subfield><subfield code="0">(DE-588)4054947-1</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Stochastisches Signal</subfield><subfield code="0">(DE-588)4140374-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Adaptive Signalverarbeitung</subfield><subfield code="0">(DE-588)4128146-9</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ingle, Vinay K.</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Kogon, Stephen M.</subfield><subfield code="e">Sonstige</subfield><subfield code="4">oth</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">Digitalisierung UB Passau - ADAM Catalogue Enrichment</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017367382&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-017367382</subfield></datafield></record></collection> |
| id | DE-604.BV035447256 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T21:35:28Z |
| institution | BVB |
| isbn | 1580536107 9781580536103 |
| language | English |
| lccn | 2005045267 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-017367382 |
| oclc_num | 58791091 |
| open_access_boolean | |
| owner | DE-Aug4 DE-83 DE-739 |
| owner_facet | DE-Aug4 DE-83 DE-739 |
| physical | xviii, 796 p. ill. 26 cm |
| publishDate | 2005 |
| publishDateSearch | 2005 |
| publishDateSort | 2005 |
| publisher | Artech House |
| record_format | marc |
| series2 | Artech House signal processing library |
| spelling | Manolakis, Dimitris G. Verfasser (DE-588)1050810430 aut Statistical and adaptive signal processing spectral estimation, signal modeling, adaptive filtering, and array processing Dimitris G. Manolakis, Vinay K. Ingle, Stephen M. Kogon Boston Artech House 2005 xviii, 796 p. ill. 26 cm txt rdacontent n rdamedia nc rdacarrier Artech House signal processing library Originally published: Boston : McGraw-Hill, c2000. Includes bibliographical references (p. 769-785) and index Signal processing Statistical methods Adaptive signal processing Signalverarbeitung (DE-588)4054947-1 gnd rswk-swf Stochastisches Signal (DE-588)4140374-5 gnd rswk-swf Adaptive Signalverarbeitung (DE-588)4128146-9 gnd rswk-swf Signalverarbeitung (DE-588)4054947-1 s Stochastisches Signal (DE-588)4140374-5 s Adaptive Signalverarbeitung (DE-588)4128146-9 s DE-604 Ingle, Vinay K. Sonstige oth Kogon, Stephen M. Sonstige oth Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017367382&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Manolakis, Dimitris G. Statistical and adaptive signal processing spectral estimation, signal modeling, adaptive filtering, and array processing Signal processing Statistical methods Adaptive signal processing Signalverarbeitung (DE-588)4054947-1 gnd Stochastisches Signal (DE-588)4140374-5 gnd Adaptive Signalverarbeitung (DE-588)4128146-9 gnd |
| subject_GND | (DE-588)4054947-1 (DE-588)4140374-5 (DE-588)4128146-9 |
| title | Statistical and adaptive signal processing spectral estimation, signal modeling, adaptive filtering, and array processing |
| title_auth | Statistical and adaptive signal processing spectral estimation, signal modeling, adaptive filtering, and array processing |
| title_exact_search | Statistical and adaptive signal processing spectral estimation, signal modeling, adaptive filtering, and array processing |
| title_full | Statistical and adaptive signal processing spectral estimation, signal modeling, adaptive filtering, and array processing Dimitris G. Manolakis, Vinay K. Ingle, Stephen M. Kogon |
| title_fullStr | Statistical and adaptive signal processing spectral estimation, signal modeling, adaptive filtering, and array processing Dimitris G. Manolakis, Vinay K. Ingle, Stephen M. Kogon |
| title_full_unstemmed | Statistical and adaptive signal processing spectral estimation, signal modeling, adaptive filtering, and array processing Dimitris G. Manolakis, Vinay K. Ingle, Stephen M. Kogon |
| title_short | Statistical and adaptive signal processing |
| title_sort | statistical and adaptive signal processing spectral estimation signal modeling adaptive filtering and array processing |
| title_sub | spectral estimation, signal modeling, adaptive filtering, and array processing |
| topic | Signal processing Statistical methods Adaptive signal processing Signalverarbeitung (DE-588)4054947-1 gnd Stochastisches Signal (DE-588)4140374-5 gnd Adaptive Signalverarbeitung (DE-588)4128146-9 gnd |
| topic_facet | Signal processing Statistical methods Adaptive signal processing Signalverarbeitung Stochastisches Signal Adaptive Signalverarbeitung |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=017367382&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT manolakisdimitrisg statisticalandadaptivesignalprocessingspectralestimationsignalmodelingadaptivefilteringandarrayprocessing AT inglevinayk statisticalandadaptivesignalprocessingspectralestimationsignalmodelingadaptivefilteringandarrayprocessing AT kogonstephenm statisticalandadaptivesignalprocessingspectralestimationsignalmodelingadaptivefilteringandarrayprocessing |