Introduction to Adaptive Filtering; Fndamentals of Adaptive Filtering; The Least-Square 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; Complex Differentiation; Quantization Effects in the LMS Algorithm; Quantization Effects in the RLS Algorithm; Kalman Filters.

This book is a fresh, broad, and systematic treatment of adaptive filtering, a subject of immense practical and theoretical value. The author illustrates extensive commonalities that exist among different classes of adaptive algorithms and even among different filtering theories. He also provides a uniform treatment of the subject matter, addressing some existing limitations, providing additional insights, and detailing extensions of current theory.

Introduction to: Discrete-time signal processing; Random variables, sequences and stochastic processes; Wiener filters; Eigenvalue of Rx-properties of the error surface; Newton and steepest-descent method; The least mean-square (LMS) algorithm; Variations of LMS algorithms; Least squares and recursive least-squares signal processing.

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 algorithms for practicing engineers.