Adaptive Filters (Google eBook)

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John Wiley & Sons, Oct 11, 2011 - Science - 824 pages
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Adaptive filtering is a topic of immense practical and theoretical value, having applications in areas ranging from digital and wireless communications to biomedical systems. This book enables 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 solutions.
  

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Contents

Preface Notation
Linear Algebra
Complex Gradients
ScalarValued Data
VectorValued Data
Summary and Notes
Problems and Computer Projects
NORMAL EQUATIONS
DataNormalized Filters
Summary and Notes
Problems and Computer Projects
Transform Domain Adaptive Filters
Efficient Block Convolution
Block and Subband Adaptive Filters
LeastSquares Criterion
Recursive LeastSquares

Orthogonality Principle
Linear Models
Constrained Estimation
LEASTSQUARES METHODS
Kalman Filter
Summary and Notes
LATTICE FILTERS
SteepestDescent Technique
Transient Behavior
LMS Algorithm
Normalized LMS Algorithm
Other LMSType Algorithms
TRANSIENT PERFORMANCE
Affine Projection Algorithm
RLS Algorithm
Problems and Computer Projects
MEANSQUARE PERFORMANCE
ROBUST FILTERS
Performance of NLMS
Performance of SignError
Performance of RLS and Other Filters
Nonstationary Environments
Performance of
Weighted Energy Conservation
LMS with Gaussian Regressors
LMS with nonGaussian Regressors
Kalman Filtering and
Order and TimeUpdate Relations
Summary and Notes
Problems and Computer Projects
Norm and Angle Preservation
Unitary Transformations
QR and Inverse QR Algorithms
Summary and Notes
Hyperbolic Rotations
Fast Array Algorithm
Regularized Prediction Problems
Fast FixedOrder Filters
Summary and Notes
Three Basic Estimation Problems
Lattice Filter Algorithms
ErrorFeedback Lattice Filters
Array Lattice Filters
Summary and Notes
Indefinite LeastSquares
RobustAdaptive Filters
Robustness Properties
Summary and Notes
References
Author Index
Subject Index
Copyright

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About the author (2011)

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.

Bibliographic information