| Description |
1 online resource (913 pages) |
| Contents |
Cover; Title; Contents; Preface; Acknowledgments; Background and Preview; 1. The Filtering Problem; 2. Linear Optimum Filters; 3. Adaptive Filters; 4. Linear Filter Structures; 5. Approaches to the Development of Linear Adaptive Filters; 6. Adaptive Beamforming; 7. Four Classes of Applications; 8. Historical Notes; Chapter 1 Stochastic Processes and Models; 1.1 Partial Characterization of a Discrete-Time Stochastic Process; 1.2 Mean Ergodic Theorem; 1.3 Correlation Matrix; 1.4 Correlation Matrix of Sine Wave Plus Noise; 1.5 Stochastic Models; 1.6 Wold Decomposition |
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1.7 Asymptotic Stationarity of an Autoregressive Process1.8 Yuleâ#x80;#x93;Walker Equations; 1.9 Computer Experiment: Autoregressive Process of Order Two; 1.10 Selecting the Model Order; 1.11 Complex Gaussian Proceses; 1.12 Power Spectral Density; 1.13 Propert ies of Power Spectral Density; 1.14 Transmission of a Stationary Process Through a Linear Filter; 1.15 Cramér Spectral Representation for a Stationary Process; 1.16 Power Spectrum Estimation; 1.17 Other Statistical Characteristics of a Stochastic Process; 1.18 Polyspectra; 1.19 Spectral-Correlation Density; 1.20 Summary and Discussion |
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ProblemsChapter 2 Wiener Filters; 2.1 Linear Optimum Filtering: Statement of the Problem; 2.2 Principle of Orthogonality; 2.3 Minimum Mean-Square Error; 2.4 Wienerâ#x80;#x93;Hopf Equations; 2.5 Error-Performance Surface; 2.6 Multiple Linear Regression Model; 2.7 Example; 2.8 Linearly Constrained Minimum-Variance Filter; 2.9 Generalized Sidelobe Cancellers; 2.10 Summary and Discussion; Problems; Chapter 3 Linear Prediction; 3.1 Forward Linear Prediction; 3.2 Backward Linear Prediction; 3.3 Levinsonâ#x80;#x93;Durbin Algorithm; 3.4 Properties of Prediction-Error Filters; 3.5 Schurâ#x80;#x93;Cohn Test |
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3.6 Autoregressive Modeling of a Stationary Stochastic Process3.7 Cholesky Factorization; 3.8 Lattice Predictors; 3.9 All-Pole, All-Pass Lattice Filter; 3.10 Joint-Process Estimation; 3.11 Predictive Modeling of Speech; 3.12 Summary and Discussion; Problems; Chapter 4 Method of Steepest Descent; 4.1 Basic Idea of the Steepest-Descent Algorithm; 4.2 The Steepest-Descent Algorithm Applied to the Wiener Filter; 4.3 Stability of the Steepest-Descent Algorithm; 4.4 Example; 4.5 The Steepest-Descent Algorithm Viewed as a Deterministic Search Method |
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4.6 Virtue and Limitation of the Steepest-Descent Algorithm4.7 Summary and Discussion; Problems; Chapter 5 Method of Stochastic Gradient Descent; 5.1 Principles of Stochastic Gradient Descent; 5.2 Application 1: Least-Mean-Square (LMS) Algorithm; 5.3 Application 2: Gradient-Adaptive Lattice Filtering Algorithm; 5.4 Other Applications of Stochastic Gradient Descent; 5.5 Summary and Discussion; Problems; Chapter 6 The Least-Mean-Square (LMS) Algorithm; 6.1 Signal-Flow Graph; 6.2 Optimality Considerations; 6.3 Applications; 6.4 Statistical Learning Theory |
| Notes |
6.5 Transient Behavior and Convergence Considerations |
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Print version record |
| Form |
Electronic book
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| ISBN |
9780273775720 |
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0273775723 |
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