《自適應濾波器原理（第五版）（英文版）》是2017年7月電子工業出版社出版的圖書，作者是（加）Simon Haykin（西蒙·赫金）。
內容簡介,圖書目錄,
內容簡介
本書是自適應信號處理領域的一本經典教材。全書共17章，系統全面、深入淺出地講述了自適應信號處理的基本理論與方法，充分反映了近年來該領域的新理論、新技術和新套用。內容包括：隨機過程與模型、維納濾波器、線性預測、最速下降法、隨機梯度下降法、最小均方（LMS）算法、歸一化LMS自適應算法及其推廣、分塊自適應濾波器、最小二乘法、遞歸最小二乘（RLS）算法、魯棒性、有限字長效應、非平衡環境下的自適應、卡爾曼濾波器、平方根自適應濾波算法、階遞歸自適應濾波算法、盲反卷積，以及它們在通信與信息系統中的套用。
圖書目錄
Contents
Background and Preview 1
1. The Filtering Problem 1
2. Linear Optimum Filters 4
3. Adaptive Filters 4
4. Linear Filter Structures 6
5. Approaches to the Development of Linear Adaptive Filters 12
6. Adaptive Beamforming 13
7. Four Classes of Applications 17
8. Historical Notes 20
Chapter 1 Stochastic Processes and Models 30
1.1 Partial Characterization of a Discrete-Time Stochastic Process 30
1.2 Mean Ergodic Theorem 32
1.3 Correlation Matrix 34
1.4 Correlation Matrix of Sine Wave Plus Noise 39
1.5 Stochastic Models 40
1.6 Wold Decomposition 46
1.7 Asymptotic Stationarity of an Autoregressive Process 49
1.8 Yule–Walker Equations 51
1.9 Computer Experiment: Autoregressive Process of Order Two 52
1.10 Selecting the Model Order 60
1.11 Complex Gaussian Processes 63
1.12 Power Spectral Density 65
1.13 Properties of Power Spectral Density 67
1.14 Transmission of a Stationary Process Through a Linear Filter 69
1.15 Cramér Spectral Representation for a Stationary Process 72
1.16 Power Spectrum Estimation 74
1.17 Other Statistical Characteristics of a Stochastic Process 77
1.18 Polyspectra 78
1.19 Spectral-Correlation Density 81
1.20 Summary and Discussion 84
Problems 85
Chapter 2 Wiener Filters 90
2.1 Linear Optimum Filtering: Statement of the Problem 90
2.2 Principle of Orthogonality 92
2.3 Minimum Mean-Square Error 96
2.4 Wiener–Hopf Equations 98
2.5 Error-Performance Surface 100
2.6 Multiple Linear Regression Model 104
2.7 Example 106
2.8 Linearly Constrained Minimum-Variance Filter 111
2.9 Generalized Sidelobe Cancellers 116
2.10 Summary and Discussion 122
Problems 124
Chapter 3 Linear Prediction 132
3.1 Forward Linear Prediction 132
3.2 Backward Linear Prediction 139
3.3 Levinson–Durbin Algorithm 144
3.4 Properties of Prediction-Error Filters 153
3.5 Schur–Cohn Test 162
3.6 Autoregressive Modeling of a Stationary Stochastic Process 164
3.7 Cholesky Factorization 167
3.8 Lattice Predictors 170
3.9 All-Pole, All-Pass Lattice Filter 175
3.10 Joint-Process Estimation 177
3.11 Predictive Modeling of Speech 181
3.12 Summary and Discussion 188
Problems 189
Chapter 4 Method of Steepest Descent 199
4.1 Basic Idea of the Steepest-Descent Algorithm 199
4.2 The Steepest-Descent Algorithm Applied to the Wiener Filter 200
4.3 Stability of the Steepest-Descent Algorithm 204
4.4 Example 209
4.5 The Steepest-Descent Algorithm Viewed as a Deterministic Search Method 221
4.6 Virtue and Limitation of the Steepest-Descent Algorithm 222
4.7 Summary and Discussion 223
Problems 224
Chapter 5 Method of Stochastic Gradient Descent 228
5.1 Principles of Stochastic Gradient Descent 228
5.2 Application 1: Least-Mean-Square (LMS) Algorithm 230
5.3 Application 2: Gradient-Adaptive Lattice Filtering Algorithm 237
5.4 Other Applications of Stochastic Gradient Descent 244
5.5 Summary and Discussion 245
Problems 246
Chapter 6 The Least-Mean-Square (LMS) Algorithm 248
6.1 Signal-Flow Graph 248
6.2 Optimality Considerations 250
6.3 Applications 252
6.4 Statistical Learning Theory 272
6.5 Transient Behavior and Convergence Considerations 283
6.6 Efficiency 286
6.7 Computer Experiment on Adaptive Prediction 288
6.8 Computer Experiment on Adaptive Equalization 293
6.9 Computer Experiment on a Minimum-Variance Distortionless-Response
Beamformer
302
6.10 Summary and Discussion 306
Problems 308
Chapter 7 Normalized Least-Mean-Square (LMS) Algorithm and Its
Generalization 315
7.1 Normalized LMS Algorithm: The Solution to a Constrained Optimization Problem 315
7.2 Stability of the Normalized LMS Algorithm 319
7.3 Step-Size Control for Acoustic Echo Cancellation 322
7.4 Geometric Considerations Pertaining to the Convergence Process for Real-Valued
Data 327
7.5 Affine Projection Adaptive Filters 330
7.6 Summary and Discussion 334
Problems 335
Chapter 8 Block-Adaptive Filters 339
8.1 Block-Adaptive Filters: Basic Ideas 340
8.2 Fast Block LMS Algorithm 344
8.3 Unconstrained Frequency-Domain Adaptive Filters 350
8.4 Self-Orthogonalizing Adaptive Filters 351
8.5 Computer Experiment on Adaptive Equalization 361
8.6 Subband Adaptive Filters 367
8.7 Summary and Discussion 375
Problems 376
Chapter 9 Method of Least-Squares 380
9.1 Statement of the Linear Least-Squares Estimation Problem 380
9.2 Data Windowing 383
9.3 Principle of Orthogonality Revisited 384
9.4 Minimum Sum of Error Squares 387
9.5 Normal Equations and Linear Least-Squares Filters 388
9.6 Time-Average Correlation Matrix ≥ 391
9.7 Reformulation of the Normal Equations in Terms of Data Matrices 393
9.8 Properties of Least-Squares Estimates 397
9.9 Minimum-Variance Distortionless Response (MVDR) Spectrum Estimation 401
9.10 Regularized MVDR Beamforming 404
9.11 Singular-Value Decomposition 409
9.12 Pseudoinverse 416
9.13 Interpretation of Singular Values and Singular Vectors 418
9.14 Minimum-Norm Solution to the Linear Least-Squares Problem 419
9.15 Normalized LMS Algorithm Viewed as the Minimum-Norm Solution to an
Underdetermined Least-Squares Estimation Problem 422
9.16 Summary and Discussion 424
Problems 425
Chapter 10 The Recursive Least-Squares (RLS) Algorithm 431
10.1 Some Preliminaries 431
10.2 The Matrix Inversion Lemma 435
10.3 The Exponentially Weighted RLS Algorithm 436
10.4 Selection of the Regularization Parameter 439
10.5 Updated Recursion for the Sum of Weighted Error Squares 441
10.6 Example: Single-Weight Adaptive Noise Canceller 443
10.7 Statistical Learning Theory 444
10.8 Efficiency 449
10.9 Computer Experiment on Adaptive Equalization 450
10.10 Summary and Discussion 453
Problems 454
Chapter 11 Robustness 456
11.1 Robustness, Adaptation, and Disturbances 456
11.2 Robustness: Preliminary Considerations Rooted in H∞ Optimization 457
11.3 Robustness of the LMS Algorithm 460
11.4 Robustness of the RLS Algorithm 465
11.5 Comparative Evaluations of the LMS and RLS Algorithms from the Perspective of
Robustness
470
11.6 Risk-Sensitive Optimality 470
11.7 Trade-Offs Between Robustness and Efficiency 472
11.8 Summary and Discussion 474
Problems 474
Chapter 12 Finite-Precision Effects 479
12.1 Quantization Errors 480
12.2 Least-Mean-Square (LMS) Algorithm 482
12.3 Recursive Least-Squares (RLS) Algorithm 491
12.4 Summary and Discussion 497
Problems 498
Chapter 13 Adaptation in Nonstationary Environments 500
13.1 Causes and Consequences of Nonstationarity 500
13.2 The System Identification Problem 501
13.3 Degree of Nonstationarity 504
13.4 Criteria for Tracking Assessment 505
13.5 Tracking Performance of the LMS Algorithm 507
13.6 Tracking Performance of the RLS Algorithm 510
13.7 Comparison of the Tracking Performance of LMS and RLS Algorithms 514
13.8 Tuning of Adaptation Parameters 518
13.9 Incremental Delta-Bar-Delta (IDBD) Algorithm 520
13.10 Autostep Method 526
13.11 Computer Experiment: Mixture of Stationary and Nonstationary Environmental
Data 530
13.12 Summary and Discussion 534
Problems 535
Chapter 14 Kalman Filters 540
14.1 Recursive Minimum Mean-Square Estimation for Scalar Random Variables 541
14.2 Statement of the Kalman Filtering Problem 544
14.3 The Innovations Process 547
14.4 Estimation of the State Using the Innovations Process 549
14.5 Filtering 555
14.6 Initial Conditions 557
14.7 Summary of the Kalman Filter 558
14.8 Optimality Criteria for Kalman Filtering 559
14.9 Kalman Filter as the Unifying Basis for RLS Algorithms 561
14.10 Covariance Filtering Algorithm 566
14.11 Information Filtering Algorithm 568
14.12 Summary and Discussion 571
Problems 572
Chapter 15 Square-Root Adaptive Filtering Algorithms 576
15.1 Square-Root Kalman Filters 576
15.2 Building Square-Root Adaptive Filters on the Two Kalman Filter Variants 582
15.3 QRD-RLS Algorithm 583
15.4 Adaptive Beamforming 591
15.5 Inverse QRD-RLS Algorithm 598
15.6 Finite-Precision Effects 601
15.7 Summary and Discussion 602
Problems 603
Chapter 16 Order-Recursive Adaptive Filtering Algorithm 607
16.1 Order-Recursive Adaptive Filters Using Least-Squares Estimation:
An Overview 608
16.2 Adaptive Forward Linear Prediction 609
16.3 Adaptive Backward Linear Prediction 612
16.4 Conversion Factor 615
16.5 Least-Squares Lattice (LSL) Predictor 618
16.6 Angle-Normalized Estimation Errors 628
16.7 First-Order State-Space Models for Lattice Filtering 632
16.8 QR-Decomposition-Based Least-Squares Lattice (QRD-LSL) Filters 637
16.9 Fundamental Properties of the QRD-LSL Filter 644
16.10 Computer Experiment on Adaptive Equalization 649
16.11 Recursive (LSL) Filters Using A Posteriori Estimation Errors 654
16.12 Recursive LSL Filters Using A Priori Estimation Errors with Error Feedback 657
16.13 Relation Between Recursive LSL and RLS Algorithms 662
16.14 Finite-Precision Effects 665
16.15 Summary and Discussion 667
Problems 669
Chapter 17 Blind Deconvolution 676
17.1 Overview of Blind Deconvolution 676
17.2 Channel Identifiability Using Cyclostationary Statistics 681
17.3 Subspace Decomposition for Fractionally Spaced Blind Identification 682
17.4 Bussgang Algorithm for Blind Equalization 696
17.5 Extension of the Bussgang Algorithm to Complex Baseband Channels 713
17.6 Special Cases of the Bussgang Algorithm 714
17.7 Fractionally Spaced Bussgang Equalizers 718
17.8 Estimation of Unknown Probability Distribution Function of Signal Source 723
17.9 Summary and Discussion 727
Problems 728
Epilogue 732
1. Robustness, Efficiency, and Complexity 732
2. Kernel-Based Nonlinear Adaptive Filtering 735
Appendix A Theory of Complex Variables 752
A.1 Cauchy–Riemann Equations 752
A.2 Cauchy’s Integral Formula 754
A.3 Laurent’s Series 756
A.4 Singularities and Residues 758
A.5 Cauchy’s Residue Theorem 759
A.6 Principle of the Argument 760
A.7 Inversion Integral for the z-Transform 763
A.8 Parseval’s Theorem 765
Appendix B Wirtinger Calculus for Computing Complex Gradients 767
B.1 Wirtinger Calculus: Scalar Gradients 767
B.2 Generalized Wirtinger Calculus: Gradient Vectors 770
B.3 Another Approach to Compute Gradient Vectors 772
B.4 Expressions for the Partial Derivatives 0f>0z and 0f>0z* 773
Appendix C Method of Lagrange Multipliers 774
C.1 Optimization Involving a Single Equality Constraint 774
C.2 Optimization Involving Multiple Equality Constraints 775
C.3 Optimum Beamformer 776
Appendix D Estimation Theory 777
D.1 Likelihood Function 777
D.2 Cramér–Rao Inequality 778
D.3 Properties of Maximum-Likelihood Estimators 779
D.4 Conditional Mean Estimator 780
Appendix E Eigenanalysis 782
E.1 The Eigenvalue Problem 782
E.2 Properties of Eigenvalues and Eigenvectors 784
E.3 Low-Rank Modeling 798
E.4 Eigenfilters 802
E.5 Eigenvalue Computations 804
Appendix F Langevin Equation of Nonequilibrium Thermodynamics 807
F.1 Brownian Motion 807
F.2 Langevin Equation 807
Appendix G Rotations and Reflections 809
G.1 Plane Rotations 809
G.2 Two-Sided Jacobi Algorithm 811
G.3 Cyclic Jacobi Algorithm 817
G.4 Householder Transformation 820
G.5 The QR Algorithm 823
Appendix H Complex Wishart Distribution 830
H.1 Definition 830
H.2 The Chi-Square Distribution as a Special Case 831
H.3 Properties of the Complex Wishart Distribution 832
H.4 Expectation of the Inverse Correlation Matrix ≥–1(n) 833
Glossary 834
Text Conventions 834
Abbreviations 837
Principal Symbols 840
Bibliography 846
Suggested Reading 861
Index 879
