Adaptive filters MNw

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. 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. 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. Ali H. Sayed. Includes Bibliographical References (p. 758-774) And Indexes. Adaptive Filtering: Algorithms and Practical Implementation, Third Edition, presents basic concepts of adaptive signal processing and filtering in a concise and straightforward manner. It concentrates on on-line algorithms whose adaptation occurs whenever a new sample of each environment signal is available. The material also illustrates block algorithms using a sub-band filtering framework whose adaptation occurs when a new block of data is available. Highlights of the new edition include: Expanded treatment of complex algorithms throughout the book New chapters on Data-Selective and Blind Adaptive Filtering An enlarged discussion of linear-constrained Wiener filters Detailed analysis of the affine projection algorithm Updated derivations and many new examples A primer on Kalman filtering in Appendix D as a complement to RLS algorithms. Algorithms are presented in a unified framework using a consistent notation that facilitates their actual implementation. The main algorithms are summarized and described in tables. Many examples address problems drawn from actual applications. The family of LMS and RLS algorithms as well as set-membership, sub-band, blind, nonlinear and IIR adaptive filtering, are covered. Problems are included at the end of chapters. Adaptive Filtering: Algorithms and Practical Implementation, Third Edition, is intended for advanced undergraduate and graduate students studying adaptive filtering and will also serve as an up-to-date and useful reference for professional engineers working in the field