Adaptive Filtering Primer With MATLAB 1st Edition By Alexander Poularikas, Zayed Ramadan 0849370434 9780849370434

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Product details:

ISBN 10: 0849370434

ISBN 13: 9780849370434

Author: Alexander D. Poularikas, Zayed M. Ramadan

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.

Adaptive Filtering Primer With MATLAB 1st Table of contents:

Chapter 1 Introduction

1.1 Signal processing

1.2 An example

1.3 Outline of the text

Chapter 2 Discrete-time signal processing

2.1 Discrete-time signals

2.2 Transform-domain representation of discrete-time signals

2.3 The Z-Transform

2.4 Discrete-time systems

Problems

Hints-solutions-suggestions

Chapter 3 Random variables, sequences, and stochastic processes

3.1 Random signals and distributions

3.2 Averages

3.3 Stationary processes

3.4 Special random signals and probability density functions

3.5 Wiener–Khintchin relations

3.6 Filtering random processes

3.7 Special types of random processes

3.8 Nonparametric spectra estimation

3.9 Parametric methods of power spectral estimations

Problems

Hints-solutions-suggestions

Chapter 4 Wiener filters

4.1 The mean-square error

4.2 The FIR Wiener filter

4.3 The Wiener solution

4.4 Wiener filtering examples

Problems

Hints-solutions-suggestions

Chapter 5 Eigenvalues of Rx — properties of the error surface

5.1 The eigenvalues of the correlation matrix

5.2 Geometrical properties of the error surface

Problems

Hints-solutions-suggestions

Chapter 6 Newton and steepest-descent method

6.1 One-dimensional gradient search method

6.2 Steepest-descent algorithm

Problems

Hints-solutions-suggestions

Chapter 7 The least mean-square (LMS) algorithm

7.1 Introduction

7.2 Derivation of the LMS algorithm

7.3 Examples using the LMS algorithm

7.4 Performance analysis of the LMS algorithm

7.5 Complex representation of LMS algorithm

Problems

Hints-solutions-suggestions

Chapter 8 Variations of LMS algorithms

8.1 The sign algorithms

8.2 Normalized LMS (NLMS) algorithm

8.3 Variable step-size LMS (VSLMS) algorithm

8.4 The leaky LMS algorithm

8.5 Linearly constrained LMS algorithm

8.6 Self-correcting adaptive filtering (SCAF)

8.7 Transform domain adaptive LMS filtering

8.8 Error normalized LMS algorithms

Problems

Hints-solutions-suggestions

Chapter 9 Least squares and recursive least-squares signal processing

9.1 Introduction to least squares

9.2 Least-square formulation

9.3 Least-squares approach

9.4 Orthogonality principle

9.5 Projection operator

9.6 Least-squares finite impulse response filter

9.7 Introduction to RLS algorithm

Problems

Hints-solutions-suggestions

Abbreviations

Bibliography

Appendix — Matrix analysis

A.1 Definitions

A.2 Special matrices

A.3 Matrix operation and formulas

A.4 Eigen decomposition of matrices

A.5 Matrix expectations

A.6 Differentiation of a scalar function with respect to a vector

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