Preface | p. xiii |

Introduction to Adaptive Filtering | p. 1 |

Introduction | p. 1 |

Adaptive Signal Processing | p. 3 |

Introduction to Adaptive Algorithms | p. 5 |

Applications | p. 8 |

Fundamentals of Adaptive Filtering | p. 15 |

Introduction | p. 15 |

Signal Representation | p. 16 |

Deterministic Signals | p. 16 |

Random Signals | p. 17 |

Ergodicity | p. 25 |

The Correlation Matrix | p. 27 |

Wiener Filter | p. 38 |

Linearly-Constrained Wiener Filter | p. 42 |

The Generalized Sidelobe Canceller | p. 45 |

Mean-Square Error Surface | p. 47 |

Bias and Consistency | p. 49 |

Newton Algorithm | p. 50 |

Steepest-Descent Algorithm | p. 52 |

Applications Revisited | p. 58 |

System Identification | p. 58 |

Signal Enhancement | p. 60 |

Signal Prediction | p. 61 |

Channel Equalization | p. 62 |

Digital Communication System | p. 66 |

Concluding Remarks | p. 68 |

The Least-Mean-Square (LMS) Algorithm | p. 79 |

Introduction | p. 79 |

The LMS Algorithm | p. 80 |

Some Properties of the LMS Algorithm | p. 82 |

Gradient Behavior | p. 83 |

Convergence Behavior of the Coefficient Vector | p. 83 |

Coefficient-Error-Vector Covariance Matrix | p. 86 |

Behavior of the Error Signal | p. 89 |

Minimum Mean-Square Error | p. 90 |

Excess Mean-Square Error and Misadjustment | p. 91 |

Transient Behavior | p. 94 |

LMS Algorithm Behavior in Nonstationary Environments | p. 96 |

Examples | p. 101 |

Analytical Examples | p. 101 |

System Identification Simulations | p. 111 |

Channel Equalization Simulations | p. 120 |

Fast Adaptation Simulations | p. 122 |

The Linearly-Constrained LMS Algorithm | p. 126 |

Concluding Remarks | p. 128 |

LMS-Based Algorithms | p. 139 |

Introduction | p. 139 |

Quantized-Error Algorithms | p. 140 |

Sign-Error Algorithm | p. 141 |

Dual-Sign Algorithm | p. 150 |

Power-of-Two Error Algorithm | p. 151 |

Sign-Data Algorithm | p. 152 |

The LMS-Newton Algorithm | p. 153 |

The Normalized LMS Algorithm | p. 157 |

The Transform-Domain LMS Algorithm | p. 159 |

The Affine Projection Algorithm | p. 169 |

Simulation Examples | p. 174 |

Signal Enhancement Simulation | p. 177 |

Signal Prediction Simulation | p. 182 |

Concluding Remarks | p. 183 |

Conventional RLS Adaptive Filter | p. 195 |

Introduction | p. 195 |

The Recursive Least-Squares Algorithm | p. 196 |

Properties of the Least-Squares Solution | p. 201 |

Orthogonality Principle | p. 201 |

Relation Between Least-Squares and Wiener Solutions | p. 204 |

Influence of the Deterministic Autocorrelation Initialization | p. 204 |

Steady-State Behavior of the Coefficient Vector | p. 205 |

Coefficient-Error-Vector Covariance Matrix | p. 208 |

Behavior of the Error Signal | p. 209 |

Excess Mean-Square Error and Misadjustment | p. 213 |

Behavior in Nonstationary Environments | p. 218 |

Simulation Examples | p. 222 |

Concluding Remarks | p. 226 |

Adaptive Lattice-Based RLS Algorithms | p. 235 |

Introduction | p. 235 |

Recursive Least-Squares Prediction | p. 236 |

Forward Prediction Problem | p. 236 |

Backward Prediction Problem | p. 240 |

Order-Updating Equations | p. 242 |

A New Parameter [delta](k, i) | p. 243 |

Order Updating of [xi superscript d subscript b subscript min](k, i) and w[subscript b](k, i) | p. 245 |

Order Updating of [xi superscript d subscript f subscript min](k, i) and w[subscript f](k, i) | p. 246 |

Order Updating of Prediction Errors | p. 247 |

Time-Updating Equations | p. 249 |

Time Updating for Prediction Coefficients | p. 249 |

Time Updating for [delta](k, i) | p. 251 |

Order Updating for [gamma](k, i) | p. 254 |

Joint-Process Estimation | p. 257 |

Time Recursions of the Least-Squares Error | p. 260 |

Normalized Lattice RLS Algorithm | p. 265 |

Basic Order Recursions | p. 265 |

Feedforward Filtering | p. 267 |

Error-Feedback Lattice RLS Algorithm | p. 269 |

Recursive Formulas for the Reflection Coefficients | p. 272 |

Lattice RLS Algorithm Based on A Priori Errors | p. 273 |

Quantization Effects | p. 275 |

Concluding Remarks | p. 280 |

Fast Transversal RLS Algorithms | p. 287 |

Introduction | p. 287 |

Recursive Least-Squares Prediction | p. 288 |

Forward Prediction Relations | p. 289 |

Backward Prediction Relations | p. 290 |

Joint-Process Estimation | p. 292 |

Stabilized Fast Transversal RLS Algorithm | p. 295 |

Concluding Remarks | p. 303 |

QR-Decomposition-Based RLS Filters | p. 309 |

Introduction | p. 309 |

Triangularization Using QR-Decomposition | p. 310 |

Initialization Process | p. 311 |

Input data matrix triangularization | p. 312 |

QR-Decomposition RLS Algorithm | p. 320 |

Systolic Array Implementation | p. 325 |

Some Implementation Issues | p. 334 |

Fast QR-RLS Algorithm | p. 335 |

Backward Prediction Problem | p. 339 |

Forward Prediction Problem | p. 341 |

Conclusions and Further Reading | p. 350 |

Adaptive IIR Filters | p. 361 |

Introduction | p. 361 |

Output-Error IIR Filters | p. 362 |

General Derivative Implementation | p. 369 |

Adaptive Algorithms | p. 371 |

Recursive least-squares algorithm | p. 371 |

The Gauss-Newton algorithm | p. 372 |

Gradient-based algorithm | p. 375 |

Alternative Adaptive Filter Structures | p. 375 |

Cascade Form | p. 376 |

Lattice Structure | p. 378 |

Parallel Form | p. 382 |

Frequency-Domain Parallel Structure | p. 383 |

Mean-Square Error Surface | p. 394 |

Influence of the Filter Structure on MSE Surface | p. 402 |

Alternative Error Formulations | p. 405 |

Equation Error Formulation | p. 405 |

The Steiglitz-McBride Method | p. 409 |

Conclusion | p. 413 |

Nonlinear Adaptive Filtering | p. 423 |

Introduction | p. 423 |

The Volterra Series Algorithm | p. 424 |

LMS Volterra Filter | p. 427 |

RLS Volterra Filter | p. 431 |

Adaptive Bilinear Filters | p. 439 |

Multilayer Perceptron Algorithm | p. 445 |

Radial Basis Function Algorithm | p. 451 |

Conclusion | p. 460 |

Subband Adaptive Filters | p. 467 |

Introduction | p. 467 |

Multirate Systems | p. 468 |

Decimation and Interpolation | p. 468 |

Filter Banks | p. 471 |

Two-Band Perfect Reconstruction Filter Banks | p. 477 |

Analysis of Two-Band Filter Banks | p. 478 |

Analysis of M-Band Filter Banks | p. 478 |

Hierarchical M-Band Filter Banks | p. 479 |

Cosine-Modulated Filter Banks | p. 479 |

Block Representation | p. 481 |

Subband Adaptive Filters | p. 481 |

Subband Identification | p. 486 |

Two-Band Identification | p. 487 |

Closed-Loop Structure | p. 488 |

Cross-Filters Elimination | p. 494 |

Fractional Delays | p. 497 |

Delayless Subband Adaptive Filtering | p. 501 |

Computational Complexity | p. 508 |

Frequency-Domain Adaptive Filtering | p. 510 |

Conclusion | p. 519 |

Quantization Effects in the LMS and RLS Algorithms | p. 527 |

Quantization Effects in the LMS Algorithm | p. 527 |

Error Description | p. 527 |

Error Models for Fixed-Point Arithmetic | p. 529 |

Coefficient-Error-Vector Covariance Matrix | p. 530 |

Algorithm Stop | p. 533 |

Mean-Square Error | p. 533 |

Floating-Point Arithmetic Implementation | p. 534 |

Floating-Point Quantization Errors in LMS Algorithm | p. 537 |

Quantization Effects in the RLS Algorithm | p. 540 |

Error Description | p. 540 |

Error Models for Fixed-Point Arithmetic | p. 543 |

Coefficient-Error-Vector Covariance Matrix | p. 544 |

Algorithm Stop | p. 548 |

Mean-Square Error | p. 549 |

Fixed-Point Implementation Issues | p. 549 |

Floating-Point Arithmetic Implementation | p. 550 |

Floating-Point Quantization errors in RLS Algorithm | p. 554 |

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