Cuốn sách này trình bày các phương pháp tiếp cận thuật toán dựa trên mô hình để ước tính độ trễ thời gian bất biến nhiễu, đặc biệt phù hợp để ước tính chênh lệch độ trễ thời gian nhỏ với độ phân giải cần thiết thấp hơn nhiều so với thời gian lấy mẫu.

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.