QRD-RLS Adaptive Filtering

QRD-RLS Adaptive Filtering covers some of the most recent developments as well as the basic concepts for a complete understanding of the QRD-RLS-based adaptive filtering algorithms. It presents this research with a clear historical perspective which highlights the underpinning theory and common moti...

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Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Apolinr̀io, Jos ̌Antonio. (Editor)
Format: Electronic
Language:English
Published: Boston, MA : Springer US, 2009.
Subjects:
Online Access:https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-0-387-09734-3
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505 0 # |a QR Decomposition: An Annotated Bibliography -- Introduction to Adaptive Filters -- Conventional and Inverse QRD-RLS Algorithms -- Fast QRD-RLS Algorithms -- QRD Least-Squares Lattice Algorithms -- Multichannel Fast QRD-RLS Algorithms -- Householder-Based RLS Algorithms -- Numerical Stability Properties -- Finite and Infinite Precision Properties of QRD-RLS Algorithms -- On Pipelined Implementations of QRD-RLS Adaptive Filters -- Weight Extraction of Fast QRD-RLS Algorithms -- Linear Constrained QRD-Based Algorithm. 
520 # # |a QRD-RLS Adaptive Filtering covers some of the most recent developments as well as the basic concepts for a complete understanding of the QRD-RLS-based adaptive filtering algorithms. It presents this research with a clear historical perspective which highlights the underpinning theory and common motivating factors that have shaped the subject. The material is divided into twelve chapters, going from fundamentals to more advanced aspects. Different algorithms are derived and presented, including basic, fast, lattice, multichannel and constrained versions. Important issues, such as numerical stability, performance in finite precision environments and VLSI oriented implementations are also addressed. All algorithms are derived using Givens rotations, although one chapter deals with implementations using Householder reflections. QRD-RLS Adaptive Filtering is a useful reference for engineers and academics in the field of adaptive filtering. 
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