About this book
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.
Editors and affiliations
- Book Title QRD-RLS Adaptive Filtering
JOSE APOLINARIO JR
- DOI https://doi.org/10.1007/978-0-387-09734-3
- Copyright Information Springer-Verlag US 2009
- Publisher Name Springer, Boston, MA
- eBook Packages Engineering Engineering (R0)
- Hardcover ISBN 978-0-387-09733-6
- Softcover ISBN 978-1-4419-3526-7
- eBook ISBN 978-0-387-09734-3
- Edition Number 1
- Number of Pages XX, 356
- Number of Illustrations 0 b/w illustrations, 0 illustrations in colour
Signal, Image and Speech Processing
Communications Engineering, Networks
Control, Robotics, Mechatronics
- Buy this book on publisher's site
From the reviews:“Starting with a review of the history and the essential concepts in (numerical) linear algebra that are essential in the development of QR decomposition, the book continues with an overview of adaptive filtering techniques, including LMS and RLS algorithms. … The chapters flow on nicely from one to the other, and the editor is to be congratulated on achieving this. The book is a useful and welcome contribution to the broad topic of numerical linear algebra.” (Andrew Dale, Zentralblatt MATH, Vol. 1170, 2009)