adaptive_filters_theory_and_applications_2nd
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Adaptive Filters. Theory and Applications. 2nd Edition
Description: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 earlier chapters of the book is modified for the various applicatio
ns 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:Preface xvii
Acknowledgments xxi
1 Introduction 1
1.1 Linear Filters 1
1.2 Adaptive Filters 2
1.3 Adaptive Filter Structures 3
1.4 Adaptation Approaches 7
1.5 Real and Complex Forms of Adaptive Filters 9
1.6 Applications 9
2 Discrete–Time Signals and Systems 28
2.1 Sequences and z–Transform 28
2.2 Parseval s Relation 32
2.3 System Function 33
2.4 Stochastic Processes 35
Problems 44
3 Wiener Filters 48
3.1 Mean–Squared Error Criterion 48
3.2 Wiener Filter Transversal, Real–Valued Case 50
3.3 Principle of Orthogonality 55
3.4 Normalized Performance Function 57
3.5 Extension to Complex–Valued Case 58
3.6 Unconstrained Wiener Filters 61
3.7 Summary and Discussion 79
Problems 80
4 Eigenanalysis and Performance Surface 90
4.1 Eigenvalues and Eigenvectors 90
4.2 Properties of Eigenvalues and Eigenvectors 91
4.3 Performance Surface 104
Problems 112
5 Search Methods 119
5.1 Method of Steepest Descent 120
5.2 Learning Curve 126
5.3 Effect of Eigenvalue Spread 130
5.4 Newton s Method 131
5.5 An Alternative Interpretation of Newton s Algorithm 133 Problems 135
6 LMS Algorithm 139
6.1 Derivation of LMS Algorithm 139
6.2 Average Tap–Weight Behavior of the LMS Algorithm 141 6.3 MSE Behavior of the LMS Algorithm 144 6.4 Computer Simulations 156
6.5 Simplified LMS Algorithms 167
6.6 Normalized LMS Algorithm 170
6.7 Affine Projection LMS Algorithm 173
6.8 Variable Step–Size LMS Algorithm 177
6.9 LMS Algorithm for Complex–Valued Signals 179
6.10 Beamforming (Revisited) 182
Problems 190
Appendix 6A: Derivation of Eq. (6.39) 205
7 Transform Domain Adaptive Filters 207
7.1 Overview of Transform Domain Adaptive Filters 208
7.2 Band–Partitioning Property of Orthogonal Transforms 210
7.3 Orthogonalization Property of Orthogonal Transforms 211
7.4 Transform Domain LMS Algorithm 213
7.5 Ideal LMS–Newton Algorithm and Its Relationship with TDLMS 215
7.6 Selection of the Transform T 216
7.7 Transforms 229
7.8 Sliding Transforms 230
7.9 Summary and Discussion 242
Problems 243
8 Block Implementation of Adaptive Filters 251
8.1 Block LMS Algorithm 252
8.2 Mathematical Background 255
8.3 The FBLMS Algorithm 260
8.4 The Partitioned FBLMS Algorithm 267
8.5 Computer Simulations 276
Problems 279
Appendix 8A: Derivation of a Misadjustment Equation for the BLMS Algorithm 285 Appendix 8B: Derivation of Misadjustment Equations for the FBLMS Algorithms 288 9 Subband Adaptive Filters 294
adaptive9.1 DFT Filter Banks 295
9.2 Complementary Filter Banks 299
9.3 Subband Adaptive Filter Structures 303
9.4 Selection of Analysis and Synthesis Filters 304
9.5 Computational Complexity 307
9.6 Decimation Factor and Aliasing 308
9.7 Low–Delay Analysis and Synthesis Filter Banks 310
9.8 A Design Procedure for Subband Adaptive Filters 313
9.9 An Example 316
Problems 319
10 IIR Adaptive Filters 322
10.1 Output Error Method 323
10.2 Equation Error Method 327
10.3 Case Study I: IIR Adaptive Line Enhancement 332
10.4 Case Study II: Equalizer Design for Magnetic Recording Channels 343 10.5 Concluding Remar
ks 349 Problems 352
11 Lattice Filters 355
11.1 Forward Linear Prediction 355
11.2 Backward Linear Prediction 357
11.3 Relationship Between Forward and Backward Predictors 359
11.4 Prediction–Error Filters 359
11.5 Properties of Prediction Errors 360
11.6 Derivation of Lattice Structure 362
11.7 Lattice as an Orthogonalization Transform 367
11.8 Lattice Joint Process Estimator 369
11.9 System Functions 370
11.10 Conversions 370
11.11 All–Pole Lattice Structure 376
11.12 Pole–Zero Lattice Structure 376
11.13 Adaptive Lattice Filter 378
11.14 Autoregressive Modeling of Random Processes 383
11.15 Adaptive Algorithms Based on Autoregressive Modeling 385 Problems 400
Appendix 11A: Evaluation of E[ua(n)xT(n)K(n)x(n)uTa (n)] 407
Appendix 11B: Evaluation of the parameter 408
12 Method of Least–Squares 410
12.1 Formulation of Least–Squares Estimation for a Linear Combiner 411 12.2 Principle of Orthogonality 412
12.3 Projection Operator 415
12.4 Standard Recursive Least–Squares Algorithm 416
12.5 Convergence Behavior of the RLS Algorithm 421
Problems 430
13 Fast RLS Algorithms 433
13.1 Least–Squares Forward Prediction 434
13.2 Least–Squares Backward Prediction 435
13.3 Least–Squares Lattice 437
13.4 RLSL Algorithm 440
13.5 FTRLS Algorithm 453
Problems 460
14 Tracking 463
14.1 Formulation of the Tracking Problem 463
14.2 Generalized Formulation of LMS Algorithm 464
14.3 MSE Analysis of the Generalized LMS Algorithm 465
14.4 Optimum Step–Size Parameters 469
14.5 Comparisons of Conventional Algorithms 471
14.6 Comparisons Based on Optimum Step–Size Parameters 475 14.7 VSLMS: An Algorithm with Optimum Tracking Behavior 477 14.8 RLS Algorithm with Variable Forgetting Factor 485
14.9 Summary 486
Problems 488
15 Echo Cancellation 492
15.1 The Problem Statement 492
15.2 Structures and Adaptive Algorithms 495
15.3 Double–Talk Detection 512
15.4 Howling Suppression 521
15.5 Stereophonic Acoustic Echo Cancellation 524
Appendix 15A: Multitaper method 542
Appendix 15B: Derivation of the Two–Channel Levinson Durbin Algorithm 549
16 Active Noise Control 551
16.1 Broadband Feedforward Single–Channel ANC 553
16.2 Narrowband Feedforward Single–Channel ANC 559
16.3 Feedback Single–Channel ANC 573
16.4 Multichannel ANC Systems 577
Appendix 16A: Derivation of Eq. (16.46) 582
Appendix 16B: Derivation of Eq. (16.53) 583
17 Synchronization and Equalization in Data Transmission Systems 584 17.1 Continuous Time Channel Model 585 17.2 Discrete Time Channel Model and Equalizer Structures 589
17.3 Timing Recovery 593
17.4 Equalizers Design and Performance Analysis 606

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