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partⅠ GENERAL INTRODUCTION1

Objectives of PartⅠ1

1 ADAPTIVE SYSTEMS3

Definition and Characteristics3

Areas of Application4

General Properties5

Open-and Closed-Loop Adaptation6

Applications of Closed-Loop Adaptation9

Example of an Adaptive System11

The Chapters Ahead13

2 THE ADAPTIVE LINEAR COMBINER15

General Description15

Input Signal and Weight Vectors16

Desired Response and Error18

The Performance Function19

Gradient and Minimum Mean-Square Error21

Example of a Performance Surface22

Alternative Expression of the Gradient24

Decorrelation of Error and Input Components26

Exercises26

partⅡ THEORY OF ADAPTATION WITH STATIONARY SIGNALS31

Objectives of PartⅡ31

3 PROPERTIES OF THE QUADRATIC PERFORMANCE SURFACE33

Normal Form of the Input Correlation Matrix34

Eigenvalues and Eigenvectors of the Input Correlation Matrix34

An Example with Two Weights36

Geometrical Significance of Eigenvectors and Eigenvalues38

A Second Example41

Exercises43

4 SEARCHING THE PERFORMANCE SURFACE46

Methods of Searching the Performance Surface46

Basic Ideas of Gradient Search Methods47

A Simple Gradient Search Algorithm and Its Solution48

Stability and Rate of Convergence49

The Learning Curve51

Gradient Search by Newton's Method52

Newton's Method in Multidimensional Space54

Gradient Search by the Method of Steepest Descent56

Comparison of Learning Curves61

Exercises63

5 GRADIENT ESTIMATION AND ITS EFFECTS ON ADAPTATION66

Gradient Component Estimation by Derivative Measurement66

The Performance Penalty68

Derivative Measurement and Performance Penalties with Multiple Weights69

Variance of the Gradient Estimate71

Effects on the Weight-Vector Solution75

Excess Mean-Square Error and Time Constants80

Misadjustment87

Comparative Performance of Newton's and Steepest-Descent Methods89

Total Misadjustment and Other Practical Considerations91

Exercises93

partⅢ ADAPTIVE ALGORITHMS AND STRUCTURES97

Objectives of PartⅢ97

6 THE LMS ALGORITHM99

Derivation of the LMS Algorithm99

Convergence of the Weight Vector101

An Example of Convergence103

Learning Curve107

Noise in the Weight-Vector Solution109

Misadjustment110

Performance112

Exercises114

7 THE z-TRANSFORM IN ADAPTIVE SIGNAL PROCESSING117

The z-Transform117

Right-and Left-Handed Sequences119

Transfer Functions120

Frequency Response122

Impulse Response and Stability124

The Inverse z-Transform126

Correlation Functions and Power Spectra128

The Performance Function131

Examples of Performance Surfaces134

Exercises137

8 OTHER ADAPTIVE ALGORITHMS AND STRUCTURES141

An Ideal:The LMS/Newton Algorithm142

Properties of the LMS/Newton Algorithm145

The Sequential Regression Algorithm147

Adaptive Recursive Filters154

Random-Search Algorithms161

Lattice Structures164

The Adaptive Lattice Predictor173

Adaptive Filters with Orthogonal Signals182

Exercises186

partⅣ APPLICATIONS193

Objectives of Part Ⅳ193

9 ADAPTIVE MODELING AND SYSTEM IDENTIFICATION195

General Description195

Adaptive Modeling of a Multipath Communication Channel200

Adaptive Modeling in Geophysieal Exploration209

Adaptive Modeling in FIR Digital Filter Synthesis212

Exercises225

10 INVERSE ADAPTIVE MODELING,DECONVOLUTION,AND EQUALIZATION231

General Description of Inverse Modeling232

Some Theoretical Examples236

Adaptive Equalization of Telephone Channels244

Adapting Poles and Zeros for IIR Digital Filter Synthesis250

Exercises264

11 ADAPTIVE CONTROL SYSTEMS270

Adaptive Model Control271

Adaptive Inverse Control280

Examples of Adaptive Inverse Control285

Plant Noise and the Filtered-X LMS Algorithm288

Inverse Control Using the Filtered-X LMS Algorithm292

Model Reference Control294

Exercises298

12 ADAPTIVE INTERFERENCE CANCELING302

Early Work in Adaptive Interference Canceling303

The Concept of Adaptive Noise Canceling303

Stationary Noise-Canceling Solutions306

Effects of Signal Components in the Reference Input311

The Adaptive Interference Canceler as a Notch Filter316

The Adaptive Interference Canceler as a High-Pass Filter323

Effects of Finite Length and Causality324

Multiple-Reference Noise Canceling327

Canceling 60-Hz Interference in Electrocardiography329

Canceling Donor-Heart Interference in Heart-Transplant Electrocardiography330

Canceling the Maternal ECG in Fetal Electrocardiography334

Canceling Noise in Speech Signals337

Canceling Echoes in Long-Distance Telephone Circuits339

Canceling Antenna Sidelobe Interference347

Canceling Periodic Interference with an Adaptive Predictor349

The Adaptive Self-Tuning Filter351

The Adaptive Line Enhancer354

Conclusion361

Exercises361

13 INTRODUCTION TO ADAPTIVE ARRAYS AND ADAPTIVE BEAMFORMING368

Sidelobe Cancellation369

Beamforming with a Pilot Signal383

Spatial Configurations388

Adaptive Algorithms391

Narrowband Experiments394

Broadband Experiments399

Exercises404

14 ANALYSIS OF ADAPTIVE BEAMFORMERS409

Performance Characteristics of Receiving Arrays409

The Griffiths LMS Beamformer412

The Frost Adaptive Beamformer415

An Adaptive Beamformer with Poles and Zeros420

Signal Cancellation and Distortion429

Frequency-Hop Spread-Spectrum Techniques442

Beamformers with Superresolution445

Exercises456

APPENDIX A A Portable Random Number Generator459

INDEX469