1st Edition

Adaptive Filtering Primer with MATLAB

    238 Pages 78 B/W Illustrations
    by CRC Press

    238 Pages
    by CRC Press

    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.

    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

    Biography

    Alexander D. Poularikas, Zayed M. Ramadan