自适应滤波器算法与实践(第三版－－英文)

Paulo S.R. Diniz

Adaptive Filtering

Algorithms and Practical

Implementation

Third Edition

123

CONTENTS

PREFACE

1 INTRODUCTION TO ADAPTIVE FILTERING

1

1.1 Introduction 1

1.2 Adaptive Signal Processing 2

1.3 Introduction to Adaptive Algorithms 4

1.4 Applications 7

1.5 References 11

2.2.3 Ergodicity 21

2.3 The Correlation Matrix 23

2.4 Wiener Filter 34

2.5 Linearly Constrained Wiener Filter 39

2.5.1 The Generalized Sidelobe Canceller 43

2.6 Mean-Square Error Surface 44

2.7 Bias and Consistency 47

2.8 Newton Algorithm 48

2.9 Steepest-Descent Algorithm 49

2.10 Applications Revisited 54

2.12 References 68

2.13 Problems 70

3 THE LEAST-MEAN-SQUARE (LMS) ALGORITHM 77

3.1 Introduction 77

3.2 The LMS Algorithm 77

3.3 Some Properties of the LMS Algorithm 79

3.3.1 Gradient Behavior 79

3.3.2 Convergence Behavior of the Coefficient Vector 80

3.3.3 Coefficient-Error-Vector Covariance Matrix 82

3.6 Examples 95

3.6.1 Analytical Examples 95

3.6.2 System Identification Simulations 107

3.6.3 Channel Equalization Simulations 113

3.6.4 Fast Adaptation Simulations 114

3.6.5 The Linearly Constrained LMS Algorithm 118

3.7 Concluding Remarks 121

3.8 References 124

3.9 Problems 126

4 LMS-BASED ALGORITHMS 131

4.3 The LMS-Newton Algorithm 143

4.4 The Normalized LMS Algorithm 145

4.5 The Transform-Domain LMS Algorithm 147

4.6 The Affine Projection Algorithm 156

4.6.1 Misadjustment in the Affine Projection Algorithm 161

4.6.2 Behavior in Nonstationary Environments 169

4.6.3 Transient Behavior 171

4.6.4 Complex Affine Projection Algorithm 173

4.7 Simulation Examples 174

5.1 Introduction 195

5.2 The Recursive Least-Squares Algorithm 195

5.3 Properties of the Least-Squares Solution 200

5.3.1 Orthogonality Principle 200

5.3.2 Relation Between Least-Squares and Wiener Solutions 201

5.3.3 Influence of the Deterministic Autocorrelation Initialization 203

5.3.4 Steady-State Behavior of the Coefficient Vector 203

5.3.5 Coefficient-Error-Vector Covariance Matrix 205

5.3.6 Behavior of the Error Signal 207

5.3.7 Excess Mean-Square Error and Misadjustment 210

6 DATA-SELECTIVE ADAPTIVE FILTERING 231

6.1 Introduction 231

6.2 Set-Membership Filtering 232

6.3 Set-Membership Normalized LMS Algorithm 234

6.4 Set-Membership Affine Projection Algorithm 237

6.4.1 A Trivial Choice for Vector

6.4.2 A Simple Vector

6.4.3 Reducing the Complexity in the Simplified SM-AP Algorithm 243

6.8.1 Set-Membership Partial-Update NLMS Algorithm 256

6.9 Simulation Examples 260

7.1 Introduction 289

7.2 Recursive Least-Squares Prediction 290

7.2.1 Forward Prediction Problem 290

7.2.2 Backward Prediction Problem 293

7.3 Order-Updating Equations 295

7.5 Joint-Process Estimation 307

7.6 Time Recursions of the Least-Squares Error 311

7.7 Normalized Lattice RLS Algorithm 313

7.7.1 Basic Order Recursions 313

7.7.2 Feedforward Filtering 315

7.8 Error-Feedback Lattice RLS Algorithm 318

7.8.1 Recursive Formulas for the Reflection Coefficients 318

7.9 Lattice RLS Algorithm Based on A Priori Errors 319

7.10 Quantization Effects 321