自适应滤波器原理（第五版）（英文版）

　　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 Discus

　　Simon Haykin：IEEE会士、加拿大皇家学会会士，毕业于英国伯明翰大学电子工程系。现为加拿大McMaster大学的Distinguished University教授，认知系统实验室主任。2002年获国际无线电科学联盟(URSI）颁发的Henry Booker金质奖章。在无线通信与信号处理领域的多个方面著述颇丰，主要研究方向为自适应信号处理与智能信号处理、无线通信与雷达技术，近年来特别关注认知无线电和认知雷达方面的研究。

　　本书是自适应信号处理领域的一本经典教材。全书共17章，系统全面、深入浅出地讲述了自适应信号处理的基本理论与方法，充分反映了近年来该领域的新理论、新技术和新应用。内容包括：随机过程与模型、维纳滤波器、线性预测、*速下降法、随机梯度下降法、*小均方（LMS）算法、归一化LMS自适应算法及其推广、分块自适应滤波器、*小二乘法、递归*小二乘（RLS）算法、鲁棒性、有限字长效应、非平衡环境下的自适应、卡尔曼滤波器、平方根自适应滤波算法、阶递归自适应滤波算法、盲反卷积，以及它们在通信与信息系统中的应用。