Adaptive Filters

ISBN-10: 0470253886
ISBN-13: 9780470253885
Edition: 2008
Authors: Ali H. Sayed
List price: $176.00 Buy it from $120.65
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Description: The textbook provides a comprehensive, thorough, and up-to-date treatment of adaptive filtering with an extensive list of problems, appendices, computer projects, figures and simulations, in addition to discussions on a number topics of current  More...

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Book details

List price: $176.00
Copyright year: 2008
Publisher: John Wiley & Sons, Incorporated
Publication date: 4/14/2008
Binding: Hardcover
Pages: 824
Size: 8.50" wide x 10.25" long x 1.50" tall
Weight: 3.542
Language: English

The textbook provides a comprehensive, thorough, and up-to-date treatment of adaptive filtering with an extensive list of problems, appendices, computer projects, figures and simulations, in addition to discussions on a number topics of current interest. Special attention is given to geometric constructions, physical intuition, linear-algebraic concepts, and vector notation. Readers will have the benefit of a gradual and solid introduction to a number of useful concepts from linear algebra and matrix theory, in addition to a detailed and uniform treatment of the subject of adaptive filtering coupled with applications to a variety of problems of practical relevance. Compared with the author's earlier publication entitled Fundamentals of Adaptive Filtering, the current book is a condensed version primarily devoted to teaching with suppression of more advanced material. Some advanced material and sections have been removed and some material has been moved into the problems. At the same time, the style and the main features of the earlier publication, which was well received by readers, has been maintained.

Preface and Acknowledgments
Notation and Symbols
Background Material
Random Variables
Variance of a Random Variable
Dependent Random Variables
Complex-Valued Random Variables
Vector-Valued Random Variables
Gaussian Random Vectors
Linear Algebra
Hermitian and Positive-Definite Matrices
Range Spaces and Nullspaces of Matrices
Schur Complements
Cholesky Factorization
QR Decomposition
Singular Value Decomposition
Kronecker Products
Complex Gradients
Cauchy-Riemann Conditions
Scalar Arguments
Vector Arguments
Optimal Estimation
Scalar-Valued Data
Estimation Without Observations
Estimation Given Dependent Observations
Orthogonality Principle
Gaussian Random Variables
Vector-Valued Data
Optimal Estimator in the Vector Case
Spherically Invariant Gaussian Variables
Equivalent Optimization Criterion
Summary and Notes
Problems and Computer Projects
Linear Estimation
Normal Equations
Mean-Square Error Criterion
Minimization by Differentiation
Minimization by Completion-of-Squares
Minimization of the Error Covariance Matrix
Optimal Linear Estimator
Orthogonality Principle
Design Examples
Orthogonality Condition
Existence of Solutions
Nonzero-Mean Variables
Linear Models
Estimation using Linear Relations
Application: Channel Estimation
Application: Block Data Estimation
Application: Linear Channel Equalization
Application: Multiple-Antenna Receivers
Constrained Estimation
Minimum-Variance Unbiased Estimation
Example: Mean Estimation
Application: Channel and Noise Estimation
Application: Decision Feedback Equalization
Application: Antenna Beamforming
Kalman Filter
Innovations Process
State-Space Model
Recursion for the State Estimator
Computing the Gain Matrix
Riccati Recursion
Covariance Form
Measurement and Time-Update Form
Summary and Notes
Problems and Computer Projects
Stochastic Gradient Algorithms
Steepest-Descent Technique
Linear Estimation Problem
Steepest-Descent Method
More General Cost Functions
Transient Behavior
Modes of Convergence
Optimal Step-Size
Weight-Error Vector Convergence
Time Constants
Learning Curve
Contour Curves of the Error Surface
Iteration-Dependent Step-Sizes
Newton?s Method
LMS Algorithm
Motivation
Instantaneous Approximation
Computational Cost
Least-Perturbation Property
Application: Adaptive Channel Estimation
Application: Adaptive Channel Equalization
Application: Decision-Feedback Equalization
Ensemble-Average Learning Curves
Normalized LMS Algorithm
Instantaneous Approximation
Computational Cost
Power Normalization
Least-Perturbation Property
Other LMS-Type Algorithms
Non-Blind Algorithms
Blind Algorithms
Some Properties
Affine Projection Algorithm
Instantaneous Approximation
Computational Cost
Least-Perturbation Property
Affine Projection Interpretation

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