Adaptive Filtering Primer with MATLAB

✍ Scribed by Alexander D. Poularikas, Zayed M. Ramadan

• Publisher: CRC Press
• Year: 2006
• Tongue: English
• Leaves: 238
• Series: Electrical Engineering Primer Series
• Edition: 1
• Category: Library

✦ Synopsis

Because of the wide use of adaptive filtering in digital signal processing and, because most of the modern electronic devices include some type of an adaptive filter, a text that brings forth the fundamentals of this field was necessary. The material and the principles presented in this book are easily accessible to engineers, scientists, and students who would like to learn the fundamentals of this field and have a background at the bachelor level.

Adaptive Filtering Primer with MATLAB® clearly explains the fundamentals of adaptive filtering supported by numerous examples and computer simulations. The authors introduce discrete-time signal processing, random variables and stochastic processes, the Wiener filter, properties of the error surface, the steepest descent method, and the least mean square (LMS) algorithm. They also supply many MATLAB® functions and m-files along with computer experiments to illustrate how to apply the concepts to real-world problems. The book includes problems along with hints, suggestions, and solutions for solving them. An appendix on matrix computations completes the self-contained coverage.

With applications across a wide range of areas, including radar, communications, control, medical instrumentation, and seismology, Adaptive Filtering Primer with MATLAB® is an ideal companion for quick reference and a perfect, concise introduction to the field

✦ Table of Contents

Content: INTRODUCTION Signal Processing An Example Outline of the Text DISCRETE-TIME SIGNAL PROCESSING Discrete Time Signals Transform-Domain Representation of Discrete-Time Signals The Z-Transform Discrete-Time Systems Problems Hints-Solutions-Suggestions RANDOM VARIABLES, SEQUENCES, AND STOCHASTIC PROCESSES Random Signals and Distributions Averages Stationary Processes Special Random Signals and Probability Density Functions Wiener-Khinchin Relations Filtering Random Processes Special Types of Random Processes Nonparametric Spectra Estimation Parametric Methods of power Spectral Estimation Problems Hints-Solutions-Suggestions WIENER FILTERS The Mean-Square Error The FIR Wiener Filter The Wiener Solution Wiener Filtering Examples Problems Hints-Solutions-Suggestions EIGENVALUES OF RX - PROPERTIES OF THE ERROR SURFACE The Eigenvalues of the Correlation Matrix Geometrical Properties of the Error Surface Problems Hints-Solutions-Suggestions NEWTON AND STEEPEST-DESCENT METHOD One-Dimensional Gradient Search Method Steepest-Descent Algorithm Problems Hints-Solutions-Suggestions THE LEAST MEAN-SQUARE (LMS) ALGORITHM Introduction Derivation of the LMS Algorithm Examples Using the LMS Algorithm Equation Performance Analysis of the LMS Algorithm Equation Learning Curve Complex Representation of LMS Algorithm Problems Hints-Solutions-Suggestions VARIATIONS OF LMS ALGORITHMS The Sign Algorithms Normalized LMS (NLMS) Algorithm Variable Step-Size LMS (VSLMS) Algorithm The Leaky LMS Algorithm Linearly Constrained LMS Algorithm Self-Correcting Adaptive Filtering (SCAF) Transform Domain Adaptive LMS Filtering Error Normalized LMS Algorithms Problems Hints-Solutions-Suggestions LEAST SQUARES AND RECURSIVE LEAST-SQUARES SIGNAL PROCESSING Introduction to Least Squares Least-Square Formulation Least-Squares Approach Orthogonality Principle Projection Operator Least-Squares Finite Impulse Response Filter Introduction to RLS Algorithm Problems Hints-Solutions-Suggestions ABBREVIATIONS BIBLIOGRAPHY APPENDIX A: MATRIX ANALYSIS INDEX

✦ Subjects

Adaptives Filter.;MATLAB.;Telecommunications;TK 7800-7882 Electronics