Presents in simple terms the fundamentals of a complicated area of signal processing. Provides a self-contained, easily understood introduction to Wiener filtering, the LMS algorithm, and the least-squares approach. Demonstrates practical application through MATLAB® programs and computer experiments. Includes abundant problems along with detailed solutions, hints, or suggestions for solving every problem in the book. Offers an appendix on matrix computation.
Table of Contents
DISCRETE-TIME SIGNAL PROCESSING
RANDOM VARIABLES, SEQUENCES, AND STOCHASTIC PROCESSES
EIGENVALUES 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
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