Adaptive Filters
(Sprache: Englisch)
The textbook provides a comprehensive, thorough, and up to date treatment of adaptive Includes solved practical computer projects that illustrate how the material developed in the textbook can be used to solve problems of practical relevance.
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Produktinformationen zu „Adaptive Filters “
The textbook provides a comprehensive, thorough, and up to date treatment of adaptive Includes solved practical computer projects that illustrate how the material developed in the textbook can be used to solve problems of practical relevance.
Klappentext zu „Adaptive Filters “
Adaptive FiltersAdaptive filtering is a topic of immense practical and theoretical value, having applications in areas ranging from digital and wireless communications to biomedical systems. Now, preserving the style and main features of the earlier award-winning publication, Fundamentals of Adaptive Filtering (2005 Terman Award), the author offers readers and instructors a concentrated, systematic, and up-to-date treatment of the subject in this valuable new book.
Adaptive Filters allows readers to gain a gradual and solid introduction to the subject, its applications to a variety of topical problems, existing limitations, and extensions of current theories. The book consists of eleven parts-each part containing a series of focused lectures and ending with bibliographic comments, problems, and computer projects with MATLAB(r) solutions available to all readers. Additional features include:
*
Numerous tables, figures, and projects
*
Special focus on geometric constructions, physical intuition, linear-algebraic concepts, and vector notation
*
Background material on random variables, linear algebra, and complex gradients collected in three introductory chapters
*
Complete solutions manual available for instructors
*
MATLAB(r) solutions available for all computer projects
Adaptive Filters offers a fresh, focused look at the subject in a manner that will entice students, challenge experts, and appeal to practitioners and instructors.
Adaptive Filters
Adaptive filtering is a topic of immense practical and theoretical value, having applications in areas ranging from digital and wireless communications to biomedical systems. Now, preserving the style and main features of the earlier award-winning publication, Fundamentals of Adaptive Filtering (2005 Terman Award), the author offers readers and instructors a concentrated, systematic, and up-to-date treatment of the subject in this valuable new book.
Adaptive Filters allows readers to gain a gradual and solid introduction to the subject, its applications to a variety of topical problems, existing limitations, and extensions of current theories. The book consists of eleven parts-each part containing a series of focused lectures and ending with bibliographic comments, problems, and computer projects with MATLAB(r) solutions available to all readers. Additional features include:
- Numerous tables, figures, and projects
- Special focus on geometric constructions, physical intuition, linear-algebraic concepts, and vector notation
- Background material on random variables, linear algebra, and complex gradients collected in three introductory chapters
- Complete solutions manual available for instructors
- MATLAB(r) solutions available for all computer projects
Adaptive Filters offers a fresh, focused look at the subject in a manner that will entice students, challenge experts, and appeal to practitioners and instructors.
Adaptive filtering is a topic of immense practical and theoretical value, having applications in areas ranging from digital and wireless communications to biomedical systems. Now, preserving the style and main features of the earlier award-winning publication, Fundamentals of Adaptive Filtering (2005 Terman Award), the author offers readers and instructors a concentrated, systematic, and up-to-date treatment of the subject in this valuable new book.
Adaptive Filters allows readers to gain a gradual and solid introduction to the subject, its applications to a variety of topical problems, existing limitations, and extensions of current theories. The book consists of eleven parts-each part containing a series of focused lectures and ending with bibliographic comments, problems, and computer projects with MATLAB(r) solutions available to all readers. Additional features include:
- Numerous tables, figures, and projects
- Special focus on geometric constructions, physical intuition, linear-algebraic concepts, and vector notation
- Background material on random variables, linear algebra, and complex gradients collected in three introductory chapters
- Complete solutions manual available for instructors
- MATLAB(r) solutions available for all computer projects
Adaptive Filters offers a fresh, focused look at the subject in a manner that will entice students, challenge experts, and appeal to practitioners and instructors.
Inhaltsverzeichnis zu „Adaptive Filters “
- Preface and Acknowledgments- Notation and Symbols
BACKGROUND MATERIAL
- A. Random Variables
- B. Linear Algebra
- C. Complex Gradients
PART I: OPTIMAL ESTIMATION
1. Scalar-Valued Data
2. Vector-Valued Data
- Summary and Notes
- Problems and Computer Projects
PART II: LINEAR ESTIMATION
3. Normal Equations
4. Orthogonality Principle
5. Linear Models
6. Constrained Estimation
7. Kalman Filter
- Summary and Notes
- Problems and Computer Projects
PART III: STOCHASTIC GRADIENT ALGORITHMS
8. Steepest-Descent Technique
9. Transient Behavior
10. LMS Algorithm
11. Normalized LMS Algorithm
12. Other LMS-Type Algorithms
13. Affine Projection Algorithm
14. RLS Algorithm
- Summary and Notes
- Problems and Computer Projects
PART IV: MEAN-SQUARE PERFORMANCE
15. Energy Conservation
15.A Interpretations of the Energy Relation
16. Performance of LMS
17. Performance of NLMS
17.A Relating NLMS to LMS
18. Performance of Sign-Error LMS
19. Performance of RLS and Other Filters
20. Nonstationary Environments
21. Tracking Performance
- Summary and Notes
- Problems and Computer Projects
PART V: TRANSIENT PERFORMANCE
22. Weighted Energy Conservation
23. LMS with Gaussian Regressors
23.A Convergence Time
24. LMS with non-Gaussian Regressors
24.A Independence and Averaging Analysis
25. Data-Normalized Filters
25.A Stability Bound
25.B Stability of NLMS
- Summary and Notes
- Problems and Computer Projects
PART VI: BLOCK ADAPTIVE FILTERS
26. Transform Domain Adaptive Filters
26.A DCT-Transformed Regressors
27. Efficient Block Convolution
28. Block and Subband Adaptive Filters
28.A Another Constrained DFT Block Filter
28.B Overlap-Add Block Adaptive Filters
- Summary and Notes
- Problems and Computer Projects
PART VII: LEAST-SQUARES METHODS
29. Least-Squares Criterion
30. Recursive Least-Squares
31. Kalman Filtering and RLS
31.A Extended RLS Algorithms
32. Order and Time-Update Relations
- Summary and Notes
-
... mehr
Problems and Computer Projects
PART VIII: ARRAY ALGORITHMS
33. Norm and Angle Preservation
34. Unitary Transformations
35. QR and Inverse QR Algorithms
35.A Array Algorithms for Kalman Filtering
- Summary and Notes
- Problems and Computer Projects
PART IX: FAST RLS ALGORITHMS
36. Hyperbolic Rotations
37. Fast Array Algorithm
37.A Chandrasekhar Filter
38. Regularized Prediction Problems
39. Fast Fixed-Order Filters
- Summary and Notes
- Problems and Computer Projects
PART X: LATTICE FILTERS
40. Three Basic Estimation Problems
41. Lattice Filter Algorithms
42. Error-Feedback Lattice Filters
43. Array Lattice Filters
- Summary and Notes
- Problems and Computer Projects
PART XI: ROBUST FILTERS
44. Indefinite Least-Squares
44.A Stationary Points
44.B Inertia Conditions
45. Robust Adaptive Filters
45.A H1 Filters
46. Robustness Properties
- Summary and Notes
- Problems and Computer Projects
REFERENCES AND INDICES
- References
- Author Index
- Subject Index
PART VIII: ARRAY ALGORITHMS
33. Norm and Angle Preservation
34. Unitary Transformations
35. QR and Inverse QR Algorithms
35.A Array Algorithms for Kalman Filtering
- Summary and Notes
- Problems and Computer Projects
PART IX: FAST RLS ALGORITHMS
36. Hyperbolic Rotations
37. Fast Array Algorithm
37.A Chandrasekhar Filter
38. Regularized Prediction Problems
39. Fast Fixed-Order Filters
- Summary and Notes
- Problems and Computer Projects
PART X: LATTICE FILTERS
40. Three Basic Estimation Problems
41. Lattice Filter Algorithms
42. Error-Feedback Lattice Filters
43. Array Lattice Filters
- Summary and Notes
- Problems and Computer Projects
PART XI: ROBUST FILTERS
44. Indefinite Least-Squares
44.A Stationary Points
44.B Inertia Conditions
45. Robust Adaptive Filters
45.A H1 Filters
46. Robustness Properties
- Summary and Notes
- Problems and Computer Projects
REFERENCES AND INDICES
- References
- Author Index
- Subject Index
... weniger
Autoren-Porträt von Ali H. Sayed
Ali H. Sayed is Professor of Electrical Engineering at UCLA, where he established and directs the Adaptive Systems Laboratory. He is a Fellow of the IEEE for his contributions to adaptive filtering and estimation algorithms. His research has attracted several recognitions including the 2003 Kuwait Prize, 2005 Terman Award, and several IEEE Best Paper Awards.
Bibliographische Angaben
- Autor: Ali H. Sayed
- 2008, 1. Auflage, 832 Seiten, Maße: 21,4 x 26,2 cm, Gebunden, Englisch
- Verlag: Wiley & Sons
- ISBN-10: 0470253886
- ISBN-13: 9780470253885
- Erscheinungsdatum: 27.05.2008
Sprache:
Englisch
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