Adaptive Filtering

In the fifth edition of this textbook, author Paulo S.R. Diniz presents updated text on the basic concepts of adaptive signal processing and adaptive filtering. He first introduces the main classes of adaptive filtering algorithms in a unified framework, using clear notations that facilitate actual implementation. Algorithms are described in tables, which are detailed enough to allow the reader to verify the covered concepts. Examples address up-to-date problems drawn from actual applications. Several chapters are expanded and a new chapter 'Kalman Filtering' is included. The book provides a concise background on adaptive filtering, including the family of LMS, affine projection, RLS, set-membership algorithms and Kalman filters, as well as nonlinear, sub-band, blind, IIR adaptive filtering, and more. Problems are included at the end of chapters. A MATLAB package is provided so the reader can solve new problems and test algorithms. The book also offers easy access to working algorithmsfor practicing engineers.

Auteur

Paulo S. R. Diniz his Ph.D. from Concordia University, Montreal, PQ, Canada, in 1984, all in electrical engineering. Since 1979, he has been with the undergraduate Department of Electronics and Computer Engineering at the UFRJ. He has also been with the graduate Program of Electrical Engineering at COPPE/UFRJ since 1984, where he is presently a Professor. He served as undergraduate course Coordinator and as Chairman of the graduate department. He is one of the three senior researchers and coordinators of the Brazilian National Excellence Center in Signal Processing. He has received the Rio de Janeiro State Scientist Award from the Governor of Rio de Janeiro State, and the Distinguished Research Scientist Award from COPPE/UFRJ (2002). From January 1991 to July 1992, he was a visiting Research Associate in the Department of Electrical and Computer Engineering of University of Victoria, Victoria, BC, Canada. In 2002 he served as Melchor Endowment Chair Professor in the Department of Electrical Engineering of the University of Notre Dame, Notre Dame, USA. He also holds a Docent position at the Signal Processing laboratory of Helsinki University of Technology, and has taught short courses at numerous institutions around the world. He has published over 200 refereed papers in some of these areas and wrote two books. He has received some awards for best papers and technical achievements. He was the Technical Program Chair of the 1995 MWSCAS held in Rio de Janeiro, Brazil. He has been on the technical committee of several international conferences including ISCAS, ICECS, EUSIPCO and MWSCAS. He has served Vice President for region 9 of the IEEE Circuits and Systems Society and as Chairman of the DSP technical committee of the same Society. He is also a Fellow of IEEE (for fundamental contributions to the design and implementation of fixed and adaptive filters and Electrical Engineering Education). He has served as associate editor for the following Journals: IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing from 1996 to 1999, IEEE Transactions on Signal Processing from 1999 to 2002, and the Circuits, Systems and Signal Processing Journal from 1998 to 2002. He was a distinguished lecturer of the IEEE Circuits and Systems Society for the years 2000 to 2001 and had served as distinguished lecturer of the IEEE Signal Processing Society in 2004.

Contenu

Introduction to Adaptive Filtering.- Fundamentals of Adaptive Filtering.- The Least-Mean-Square (LMS) Algorithm.- LMS-Based Algorithms.- LMS-Based Algorithms.- Conventional RLS Adaptive Filter.- Set-Membership 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.- Kalman Filtering.- Complex Differentiation.- Quantization Effects in the LMS Algorithm.- Quantization Effects in the RLS Algorithm.- Analysis of Set-Membership Affine Projection Algorithm.- Index.