Adaptive filters [[electronic resource] ] : theory and applications / / Behrouz Farhang-Boroujeny |
Autore | Farhang-Boroujeny B |
Edizione | [2nd ed.] |
Pubbl/distr/stampa | Chichester, West Sussex, U.K., : Wiley, [2013] |
Descrizione fisica | xx, 778 p. : ill |
Disciplina | 621.3815/324 |
Soggetto topico |
Adaptive filters
Adaptive signal processing |
ISBN |
1-118-59134-8
1-118-59135-6 1-118-59133-X 1-299-46521-8 |
Classificazione |
547.1
621.3815/324 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Record Nr. | UNINA-9910796100203321 |
Farhang-Boroujeny B
![]() |
||
Chichester, West Sussex, U.K., : Wiley, [2013] | ||
![]() | ||
Lo trovi qui: Univ. Federico II | ||
|
Adaptive filters : theory and applications / / Behrouz Farhang-Boroujeny |
Autore | Farhang-Boroujeny B |
Edizione | [2nd ed.] |
Pubbl/distr/stampa | Chichester, West Sussex, U.K., : Wiley, [2013] |
Descrizione fisica | xx, 778 p. : ill |
Disciplina | 621.3815/324 |
Soggetto topico |
Adaptive filters
Adaptive signal processing |
ISBN |
1-118-59134-8
1-118-59135-6 1-118-59133-X 1-299-46521-8 |
Classificazione |
547.1
621.3815/324 |
Formato | Materiale a stampa ![]() |
Livello bibliografico | Monografia |
Lingua di pubblicazione | eng |
Nota di contenuto |
Cover -- Title Page -- Copyright -- Contents -- Preface -- Acknowledgments -- Chapter 1 Introduction -- 1.1 Linear Filters -- 1.2 Adaptive Filters -- 1.3 Adaptive Filter Structures -- 1.4 Adaptation Approaches -- 1.4.1 Approach Based on Wiener Filter Theory -- 1.4.2 Method of Least-Squares -- 1.5 Real and Complex Forms of Adaptive Filters -- 1.6 Applications -- 1.6.1 Modeling -- 1.6.2 Inverse Modeling -- 1.6.3 Linear Prediction -- 1.6.4 Interference Cancellation -- Chapter 2 Discrete-Time Signals and Systems -- 2.1 Sequences and z-Transform -- 2.2 Parseval's Relation -- 2.3 System Function -- 2.4 Stochastic Processes -- 2.4.1 Stochastic Averages -- 2.4.2 z-Transform Representations -- 2.4.3 The Power Spectral Density -- 2.4.4 Response of Linear Systems to Stochastic Processes -- 2.4.5 Ergodicity and Time Averages -- Problems -- Chapter 3 Wiener Filters -- 3.1 Mean-Squared Error Criterion -- 3.2 Wiener Filter-Transversal, Real-Valued Case -- 3.3 Principle of Orthogonality -- 3.4 Normalized Performance Function -- 3.5 Extension to Complex-Valued Case -- 3.6 Unconstrained Wiener Filters -- 3.6.1 Performance Function -- 3.6.2 Optimum Transfer Function -- 3.6.3 Modeling -- 3.6.4 Inverse Modeling -- 3.6.5 Noise Cancellation -- 3.7 Summary and Discussion -- Problems -- Chapter 4 Eigenanalysis and Performance Surface -- 4.1 Eigenvalues and Eigenvectors -- 4.2 Properties of Eigenvalues and Eigenvectors -- 4.3 Performance Surface -- Problems -- Chapter 5 Search Methods -- 5.1 Method of Steepest Descent -- 5.2 Learning Curve -- 5.3 Effect of Eigenvalue Spread -- 5.4 Newton's Method -- 5.5 An Alternative Interpretation of Newton's Algorithm -- Problems -- Chapter 6 LMS Algorithm -- 6.1 Derivation of LMS Algorithm -- 6.2 Average Tap-Weight Behavior of the LMS Algorithm -- 6.3 MSE Behavior of the LMS Algorithm -- 6.3.1 Learning Curve.
6.3.2 Weight-Error Correlation Matrix -- 6.3.3 Excess MSE and Misadjustment -- 6.3.4 Stability -- 6.3.5 The Effect of Initial Values of Tap Weights on the Transient Behavior of the LMS Algorithm -- 6.4 Computer Simulations -- 6.4.1 System Modeling -- 6.4.2 Channel Equalization -- 6.4.3 Adaptive Line Enhancement -- 6.4.4 Beamforming -- 6.5 Simplified LMS Algorithms -- 6.6 Normalized LMS Algorithm -- 6.7 Affine Projection LMS Algorithm -- 6.8 Variable Step-Size LMS Algorithm -- 6.9 LMS Algorithm for Complex-Valued Signals -- 6.10 Beamforming (Revisited) -- 6.11 Linearly Constrained LMS Algorithm -- 6.11.1 Statement of the Problem and Its Optimal Solution -- 6.11.2 Update Equations -- 6.11.3 Extension to the Complex-Valued Case -- Problems -- Chapter 7 Transform Domain Adaptive Filters -- 7.1 Overview of Transform Domain Adaptive Filters -- 7.2 Band-Partitioning Property of Orthogonal Transforms -- 7.3 Orthogonalization Property of Orthogonal Transforms -- 7.4 Transform Domain LMS Algorithm -- 7.5 Ideal LMS-Newton Algorithm and Its Relationship with TDLMS -- 7.6 Selection of the Transform T -- 7.6.1 A Geometrical Interpretation -- 7.6.2 A Useful Performance Index -- 7.6.3 Improvement Factor and Comparisons -- 7.6.4 Filtering View -- 7.7 Transforms -- 7.8 Sliding Transforms -- 7.8.1 Frequency Sampling Filters -- 7.8.2 Recursive Realization of Sliding Transforms -- 7.8.3 Nonrecursive Realization of Sliding Transforms -- 7.8.4 Comparison of Recursive and Nonrecursive Sliding Transforms -- 7.9 Summary and Discussion -- Problems -- Chapter 8 Block Implementation of Adaptive Filters -- 8.1 Block LMS Algorithm -- 8.2 Mathematical Background -- 8.2.1 Linear Convolution Using the Discrete Fourier Transform -- 8.2.2 Circular Matrices -- 8.2.3 Window Matrices and Matrix Formulation of the Overlap-Save Method -- 8.3 The FBLMS Algorithm. 8.3.1 Constrained and Unconstrained FBLMS Algorithms -- 8.3.2 Convergence Behavior of the FBLMS Algorithm -- 8.3.3 Step-Normalization -- 8.3.4 Summary of the FBLMS Algorithm -- 8.3.5 FBLMS Misadjustment Equations -- 8.3.6 Selection of the Block Length -- 8.4 The Partitioned FBLMS Algorithm -- 8.4.1 Analysis of the PFBLMS Algorithm -- 8.4.2 PFBLMS Algorithm with M > -- L -- 8.4.3 PFBLMS Misadjustment Equations -- 8.4.4 Computational Complexity and Memory Requirement -- 8.4.5 Modified Constrained PFBLMS Algorithm -- 8.5 Computer Simulations -- Problems -- Chapter 9 Subband Adaptive Filters -- 9.1 DFT Filter Banks -- 9.1.1 Weighted Overlap-Add Method for Realization of DFT Analysis Filter Banks -- 9.1.2 Weighted Overlap-Add Method for Realization of DFT Synthesis Filter Banks -- 9.2 Complementary Filter Banks -- 9.3 Subband Adaptive Filter Structures -- 9.4 Selection of Analysis and Synthesis Filters -- 9.5 Computational Complexity -- 9.6 Decimation Factor and Aliasing -- 9.7 Low-Delay Analysis and Synthesis Filter Banks -- 9.7.1 Design Method -- 9.7.2 Filters Properties -- 9.8 A Design Procedure for Subband Adaptive Filters -- 9.9 An Example -- 9.10 Comparison with FBLMS Algorithm -- Problems -- Chapter 10 IIR Adaptive Filters -- 10.1 Output Error Method -- 10.2 Equation Error Method -- 10.3 Case Study I: IIR Adaptive Line Enhancement -- 10.3.1 IIR ALE Filter, W(z) -- 10.3.2 Performance Functions -- 10.3.3 Simultaneous Adaptation of s and w -- 10.3.4 Robust Adaptation of w -- 10.3.5 Simulation Results -- 10.4 Case Study II: Equalizer Design for Magnetic Recording Channels -- 10.4.1 Channel Discretization -- 10.4.2 Design Steps -- 10.4.3 FIR Equalizer Design -- 10.4.4 Conversion from FIR into IIR Equalizer -- 10.4.5 Conversion from z Domain into s Domain -- 10.4.6 Numerical Results -- 10.5 Concluding Remarks -- Problems -- Chapter 11 Lattice Filters. 11.1 Forward Linear Prediction -- 11.2 Backward Linear Prediction -- 11.3 Relationship Between Forward and Backward Predictors -- 11.4 Prediction-Error Filters -- 11.5 Properties of Prediction Errors -- 11.6 Derivation of Lattice Structure -- 11.7 Lattice as an Orthogonalization Transform -- 11.8 Lattice Joint Process Estimator -- 11.9 System Functions -- 11.10 Conversions -- 11.10.1 Conversion Between Lattice and Transversal Predictors -- 11.10.2 Levinson-Durbin Algorithm -- 11.10.3 Extension of Levinson-Durbin Algorithm -- 11.11 All-Pole Lattice Structure -- 11.12 Pole-Zero Lattice Structure -- 11.13 Adaptive Lattice Filter -- 11.13.1 Discussion and Simulations -- 11.14 Autoregressive Modeling of Random Processes -- 11.15 Adaptive Algorithms Based on Autoregressive Modeling -- 11.15.1 Algorithms -- 11.15.2 Performance Analysis -- 11.15.3 Simulation Results and Discussion -- Problems -- Chapter 12 Method of Least-Squares -- 12.1 Formulation of Least-Squares Estimation for a Linear Combiner -- 12.2 Principle of Orthogonality -- 12.3 Projection Operator -- 12.4 Standard Recursive Least-Squares Algorithm -- 12.4.1 RLS Recursions -- 12.4.2 Initialization of the RLS Algorithm -- 12.4.3 Summary of the Standard RLS Algorithm -- 12.5 Convergence Behavior of the RLS Algorithm -- 12.5.1 Average Tap-Weight Behavior of the RLS Algorithm -- 12.5.2 Weight-Error Correlation Matrix -- 12.5.3 Learning Curve -- 12.5.4 Excess MSE and Misadjustment -- 12.5.5 Initial Transient Behavior of the RLS Algorithm -- Problems -- Chapter 13 Fast RLS Algorithms -- 13.1 Least-Squares Forward Prediction -- 13.2 Least-Squares Backward Prediction -- 13.3 Least-Squares Lattice -- 13.4 RLSL Algorithm -- 13.4.1 Notations and Preliminaries -- 13.4.2 Update Recursion for the Least-Squares Error Sums -- 13.4.3 Conversion Factor -- 13.4.4 Update Equation for Conversion Factor. 13.4.5 Update Equation for Cross-Correlations -- 13.4.6 RLSL Algorithm Using A Posteriori Errors -- 13.4.7 RLSL Algorithm with Error Feedback -- 13.5 FTRLS Algorithm -- 13.5.1 Derivation of the FTRLS Algorithm -- 13.5.2 Summary of the FTRLS Algorithm -- 13.5.3 Stabilized FTRLS Algorithm -- Problems -- Chapter 14 Tracking -- 14.1 Formulation of the Tracking Problem -- 14.2 Generalized Formulation of LMS Algorithm -- 14.3 MSE Analysis of the Generalized LMS Algorithm -- 14.4 Optimum Step-Size Parameters -- 14.5 Comparisons of Conventional Algorithms -- 14.6 Comparisons Based on Optimum Step-Size Parameters -- 14.7 VSLMS: An Algorithm with Optimum Tracking Behavior -- 14.7.1 Derivation of VSLMS Algorithm -- 14.7.2 Variations and Extensions -- 14.7.3 Normalization of the Parameter ρ -- 14.7.4 Computer Simulations -- 14.8 RLS Algorithm with Variable Forgetting Factor -- 14.9 Summary -- Problems -- Chapter 15 Echo Cancellation -- 15.1 The Problem Statement -- 15.2 Structures and Adaptive Algorithms -- 15.2.1 Normalized LMS (NLMS) Algorithm -- 15.2.2 Affine Projection LMS (APLMS) Algorithm -- 15.2.3 Frequency Domain Block LMS Algorithm -- 15.2.4 Subband LMS Algorithm -- 15.2.5 LMS-Newton Algorithm -- 15.2.6 Numerical Results -- 15.3 Double-Talk Detection -- 15.3.1 Coherence Function -- 15.3.2 Double-Talk Detection Using the Coherence Function -- 15.3.3 Numerical Evaluation of the Coherence Function -- 15.3.4 Power-Based Double-Talk Detectors -- 15.3.5 Numerical Results -- 15.4 Howling Suppression -- 15.4.1 Howling Suppression Through Notch Filtering -- 15.4.2 Howling Suppression by Spectral Shift -- 15.5 Stereophonic Acoustic Echo Cancellation -- 15.5.1 The Fundamental Problem -- 15.5.2 Reducing Coherence Between x1(n) and x2(n) -- 15.5.3 The LMS-Newton Algorithm for Stereophonic Systems -- Chapter 16 Active Noise Control. 16.1 Broadband Feedforward Single-Channel ANC. |
Record Nr. | UNINA-9910821285903321 |
Farhang-Boroujeny B
![]() |
||
Chichester, West Sussex, U.K., : Wiley, [2013] | ||
![]() | ||
Lo trovi qui: Univ. Federico II | ||
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