Fundamentals of Adaptive Filtering provides a comprehensive introduction to the theory, analysis, and design of adaptive filtering algorithms for modern signal processing. It covers optimal and linear estimation, steepest-descent, stochastic-gradient (LMS), and recursive least-squares (RLS) algorithms. The book examines convergence, steady-state and transient performance, tracking capability, finite-precision effects, block adaptive filters, lattice and Laguerre filters, and robust adaptive filtering methods. Through rigorous mathematical analysis and practical examples, it serves as a valuable reference for graduate students, researchers, and engineers in signal processing, communications, and control systems.

Ngôn ngữ:	eng
Tác giả:	Sayed, Ali H.
Thông tin nhan đề:	Fundamentals of adaptive filtering
Nhà xuất bản:	Wiley-Interscience
Loại hình:	Giáo trình sách, tham khảo / Bộ sưu tập: Điện tử
Mô tả vật lý:	1125tr.
Năm xuất bản:	2003