UID:
almafu_9959328364302883
Umfang:
1 online resource (xxx, 786 pages) :
,
illustrations
ISBN:
9780470374122
,
0470374128
,
9780470374115
,
047037411X
,
9780470253885
,
0470253886
Inhalt:
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. This book consists of eleven parts - each part containing a series of focused lectures and ending with bibliographic comments, problems, and computer projects with MATLAB[registered] 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; and, complete solutions manual available for instructors MATLAB[registered] 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.
Anmerkung:
Cover13; -- Title13; -- Copyright13; -- Dedication13; -- Contents13; -- Preface -- Notation -- Acknowledgments -- Background Material 13; -- A Random Variables -- A.1 Variance of a Random Variable -- A.2 Dependent Random Variables -- A.3 Complex-Valued Random Variables -- A.4 Vector-Valued Random Variables -- A.5 Gaussian Random Vectors -- B Linear Algebra -- B.1 Hermitian and Positive-Definite Matrices -- B.2 Range Spaces and Nullspaces of Matrices -- B.3 Schur Complements -- B.4 Cholesky Factorization -- B.5 QR Decomposition -- B.6 Singular Value Decomposition -- B.7 Kronecker Products -- C Complex Gradients -- C.1 Cauchy-Riemann Conditions -- C.2 Scalar Arguments -- C.3 Vector Arguments -- Part I: Optimal Estimation13; -- 113;Scalar-Valued Data -- 1.1 Estimation Without Observations -- 1.2 Estimation Given Dependent Observations -- 1.3 Orthogonality Principle -- 1.4 Gaussian Random Variables -- 213;Vector-Valued Data -- 2.1 Optimal Estimator in the Vector Case -- 2.2 Spherically Invariant Gaussian Variables -- 2.3 Equivalent Optimization Criterion -- Summary and Notes -- Problems and Computer Projects -- Part II: Linear Estimation13; -- 3 Normal Equations13; -- 3.1 Mean-Square Error Criterion -- 3.2 Minimization by Differentiation -- 3.3 Minimization by Completion-of-Squares -- 3.4 Minimization of the Error Covariance Matrix -- 3.5 Optimal Linear Estimator -- 4 Orthogonality Principle13; -- 4.1 Design Examples -- 4.2 Orthogonality Condition -- 4.3 Existence of Solutions -- 4.4 Nonzero-Mean Variables -- 5 Linear Models13; -- 5.1 Estimation using Linear Relations -- 5.2 Application: Channel Estimation -- 5.3 Application: Block Data Estimation -- 5.4 Application: Linear Channel Equalization -- 5.5 Application: Multiple-Antenna Receivers -- 6 Constrained Estimation13; -- 6.1 Minimum-Variance Unbiased Estimation -- 6.2 Example: Mean Estimation -- 6.3 Application: Channel and Noise Estimation -- 6.4 Application: Decision Feedback Equalization -- 6.5 Application: Antenna Beamforming -- 7 Kalman Filter13; -- 7.1 Innovations Process -- 7.2 State-Space Model -- 7.3 Recursion for the State Estimator -- 7.4 Computing the Gain Matrix -- 7.5 Riccati Recursion -- 7.6 Covariance Form -- 7.7 Measurement and Time-Update Form -- Summary and Notes -- Problems and Computer Projects -- Part III: Stochastic Gradient Algorithms13; -- 8 Steepest8211;Descent Technique13; -- 8.1 Linear Estimation Problem -- 8.2 Steepest-Descent Method -- 8.3 More General Cost Functions -- 9 Transient Behavior13; -- 9.1 Modes of Convergence -- 9.2 Optimal Step-Size -- 9.3 Weight-Error Vector Convergence -- 9.4 Time Constants -- 9.5 Learning Curve -- 9.6 Contour Curves of the Error Surface -- 9.7 Iteration-Dependent Step-Sizes -- 9.8 Newton8217;s Method -- 10 LMS Algorithm13; -- 10.1 Motivation -- 10.2 Instantaneous Approximation -- 10.3 Computational Cost -- 10.4 Least-Perturbation Property -- 10.5 Application: Adaptive Channel Estimation -- 10.6 Application: Adaptive Channel Equalization -- 10.7 Application: Decision-Feedback Equalization -- 10.8 Ensemble-Average Learning Curves -- 11 Normalized LMS Algorithm13; -- T$137.
Weitere Ausg.:
Print version: Sayed, Ali H. Adaptive filters. Hoboken, N.J. : Wiley-Interscience : IEEE Press, ©2008 ISBN 9780470253885
Weitere Ausg.:
ISBN 0470253886
Sprache:
Englisch
Fachgebiete:
Technik
Schlagwort(e):
Electronic books.
;
Electronic books.
;
Electronic books.
;
Electronic books.
DOI:
10.1002/9780470374122
URL:
https://onlinelibrary.wiley.com/doi/book/10.1002/9780470374122
URL:
https://onlinelibrary.wiley.com/doi/book/10.1002/9780470374122
URL:
https://onlinelibrary.wiley.com/doi/book/10.1002/9780470374122
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