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Adaptive Filtering [electronic resource] :Algorithms and Practical Implementation / by Paulo S. R. Diniz.

by Diniz, Paulo S. R [author.]; SpringerLink (Online service).
Material type: materialTypeLabelBookPublisher: Boston, MA : Springer US, 2008.Description: XXIV, 627 p. online resource.ISBN: 9780387686066.Subject(s): Engineering | Physics | Control engineering systems | Computer engineering | Telecommunication | Systems engineering | Engineering | Signal, Image and Speech Processing | Circuits and Systems | Communications Engineering, Networks | Control , Robotics, Mechatronics | Complexity | Electrical EngineeringOnline resources: Click here to access online
Contents:
To Adaptive Filtering -- Fundamentals of Adaptive Filtering -- The Least-Mean-Square (LMS) Algorithm -- Lms-Based Algorithms -- Conventional Rls Adaptive Filter -- Data-Selective Adaptive Filtering -- Adaptive Lattice-Based Rls Algorithms -- Fast Transversal Rls Algorithms -- Qr-Decomposition-Based Rls Filters -- Adaptive Iir Filters -- Nonlinear Adaptive Filtering -- Subband Adaptive Filters -- Blind Adaptive Filtering.
In: Springer eBooksSummary: 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.
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To Adaptive Filtering -- Fundamentals of Adaptive Filtering -- The Least-Mean-Square (LMS) Algorithm -- Lms-Based Algorithms -- Conventional Rls Adaptive Filter -- Data-Selective Adaptive Filtering -- Adaptive Lattice-Based Rls Algorithms -- Fast Transversal Rls Algorithms -- Qr-Decomposition-Based Rls Filters -- Adaptive Iir Filters -- Nonlinear Adaptive Filtering -- Subband Adaptive Filters -- Blind Adaptive Filtering.

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.

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