General

Authors

Search


Committee login



 
 

 


 

 

Forthcoming

Small thumbnail

Baidu SEO

Challenges and Intricacies of Marketing in China

Small thumbnail

Asymmetric Alliances and Information Systems

Issues and Prospects

Small thumbnail

Technicity vs Scientificity

Complementarities and Rivalries

Small thumbnail

Freshwater Fishes

250 Million Years of Evolutionary History

Small thumbnail

Biostatistics and Computer-based Analysis of Health Data using SAS

Biostatistics and Health Science Set

Small thumbnail

Predictive Control

Small thumbnail

Fundamentals of Advanced Mathematics 1

Categories, Algebraic Structures, Linear and Homological Algebra

Small thumbnail

Swelling Concrete in Dams and Hydraulic Structures

DSC 2017

Small thumbnail

The Chemostat

Mathematical Theory of Microorganims Cultures

Small thumbnail

Earthquake Occurrence

Short- and Long-term Models and their Validation

Small thumbnail

Proportionate-type Normalized Least Mean Square Algorithms

FOCUS Series in Digital Signal and Image Processing

Kevin Wagner, Radar Division of the Naval Research Laboratory, Washington DC, USA Milos Doroslovacki, George Washington University, USA

ISBN: 9781848214705

Publication Date: June 2013   Hardback   192 pp.

95.00 USD


Add to cart

eBooks


Ebook Ebook

Description

The topic of this book is proportionate-type normalized least mean square (PtNLMS) adaptive filtering algorithms, which attempt to estimate an unknown impulse response by adaptively giving gains proportionate to an estimate of the impulse response and the current measured error. These algorithms offer low computational complexity and fast convergence times for sparse impulse responses in network and acoustic echo cancellation applications.
New PtNLMS algorithms are developed by choosing gains that optimize user-defined criteria, such as mean square error, at all times. PtNLMS algorithms are extended from real-valued signals to complex-valued signals. The computational complexity of the presented algorithms is examined.

Contents

1. Introduction to PtNLMS Algorithms.
2. LMS Analysis Techniques.
3. PtNLMS Analysis Techniques.
4. Algorithms Designed Based on Minimization of User-Defined Criteria.
5. Probability Density of WD for PtNLMS Algorithms.
6. Adaptive Step-Size PtNLMS Algorithms.
7. Complex PtNLMS Algorithms.
8. Computational Complexity for PtNLMS Algorithms.

About the Authors

Kevin Wagner has been a physicist with the Radar Division of the Naval Research Laboratory, Washington, DC, USA since 2001. His research interests are in the area of adaptive signal processing and non-convex optimization.
Milos Doroslovacki has been with the Department of Electrical and Computer Engineering at George Washington University, USA since 1995, where he is now an Associate Professor. His main research interests are in the fields of adaptive signal processing, communication signals and systems, discrete-time signal and system theory, and wavelets and their applications.

Downloads

DownloadTables of Contents - PDF File - 59 Kb

































0.01837 s.