Adaptive filters : theory and applications / Behrouz Farhang-Boroujeny.

Format:

Book

Author/Creator:

Farhang-Boroujeny, B.

Language:

English

Subjects (All):

Adaptive filters.

Adaptive signal processing.

Physical Description:

xx, 778 p. : ill.

Edition:

2nd ed.

Place of Publication:

Chichester, West Sussex, U.K. : Wiley, [2013]

Summary:

This second edition of Adaptive Filters: Theory and Applications has been updated throughout to reflect the latest developments in this field; notably an increased coverage given to the practical applications of the theory to illustrate the much broader range of adaptive filters applications developed in recent years. The book offers an easy to understand approach to the theory and application of adaptive filters by clearly illustrating how the theory explained in the early chapters of the book is modified for the various applications discussed in detail in later chapters. This integrated approach makes the book a valuable resource for graduate students; and the inclusion of more advanced applications including antenna arrays and wireless communications makes it a suitable technical reference for engineers, practitioners and researchers. Key features: Offers a thorough treatment of the theory of adaptive signal processing; incorporating new material on transform domain, frequency domain, subband adaptive filters, acoustic echo cancellation and active noise control. Provides an in-depth study of applications which now includes extensive coverage of OFDM, MIMO and smart antennas. Contains exercises and computer simulation problems at the end of each chapter. Includes a new companion website hosting MATLAB® simulation programs which complement the theoretical analyses, enabling the reader to gain an in-depth understanding of the behaviours and properties of the various adaptive algorithms.

Contents:

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

Chapter 4 Eigenanalysis and Performance Surface

4.1 Eigenvalues and Eigenvectors

4.2 Properties of Eigenvalues and Eigenvectors

4.3 Performance Surface

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

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

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

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 &gt

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

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

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

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

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

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

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

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.

Notes:

Includes bibliographical references and index.

Description based on publisher supplied metadata and other sources.

ISBN:

1-118-59134-8

1-118-59135-6

1-118-59133-X

1-299-46521-8

OCLC:

830837650