Adaptive Filtering Primer With MATLAB 1st edition by Alexander Poularikas, Zayed Ramadan ISBN 0849370434 978-0849370434

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ISBN 10: 0849370434
ISBN 13: 978-0849370434
Author: Alexander Poularikas, Zayed 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:

Preface

• Purpose of the Book
• Target Audience
• How to Use MATLAB with the Book
• Key Features of MATLAB for Adaptive Filtering

Chapter 1: Introduction to Adaptive Filtering
1.1. Overview of Adaptive Filtering
1.2. Applications of Adaptive Filters
1.3. Importance of Adaptive Algorithms in Signal Processing
1.4. Basic Concepts of Filtering
1.5. Mathematical Representation of Adaptive Filters
1.6. Types of Adaptive Filters: FIR and IIR

Chapter 2: Basics of Signal Processing
2.1. Discrete-Time Signals and Systems
2.2. Linear Systems and Convolution
2.3. Frequency Domain Analysis of Signals
2.4. Signal Denoising and Noise Reduction
2.5. Sampling Theorem and Aliasing
2.6. MATLAB for Signal Processing

Chapter 3: Fundamentals of Adaptive Filtering
3.1. Adaptive Filtering Principles
3.2. Error and Cost Function Concepts
3.3. Wiener Filter and Optimal Filtering
3.4. Least Mean Squares (LMS) Algorithm
3.5. Recursive Least Squares (RLS) Algorithm
3.6. Adaptive Filter Performance Metrics

Chapter 4: LMS Algorithm
4.1. Derivation of the LMS Algorithm
4.2. Convergence Properties of LMS
4.3. Practical Considerations for LMS Implementation
4.4. MATLAB Code for LMS Algorithm
4.5. Simulations and Examples

Chapter 5: RLS Algorithm
5.1. Introduction to RLS Filtering
5.2. Derivation of the RLS Algorithm
5.3. Computational Complexity and Efficiency
5.4. Comparison with LMS Algorithm
5.5. MATLAB Implementation of RLS
5.6. Applications of RLS Filters

Chapter 6: Advanced Adaptive Algorithms
6.1. Affine Projection Algorithm (APA)
6.2. Kalman Filtering
6.3. Recursive Least Squares with Sliding Window
6.4. Normalized LMS (NLMS) Algorithm
6.5. Variable Step-Size LMS Algorithm
6.6. Comparison of Algorithms: When to Use Which?

Chapter 7: Adaptive Filtering in System Identification
7.1. System Identification Theory
7.2. Adaptive Filters for Modeling Dynamic Systems
7.3. Recursive Identification Techniques
7.4. Application of Adaptive Filters in System Identification
7.5. MATLAB Example: System Identification

Chapter 8: Adaptive Filtering in Noise Cancellation
8.1. Basics of Noise Cancellation
8.2. Adaptive Filters for Active Noise Control
8.3. Speech and Audio Enhancement using Adaptive Filters
8.4. Echo Cancellation in Communication Systems
8.5. MATLAB Example: Noise Cancellation Application

Chapter 9: Adaptive Filtering in Communication Systems
9.1. Adaptive Filtering in Equalization
9.2. Adaptive Filters for Channel Estimation
9.3. Interference Cancellation in Communication Networks
9.4. Adaptive Beamforming in Antenna Arrays
9.5. MATLAB Example: Adaptive Equalizer

Chapter 10: Real-Time Adaptive Filtering
10.1. Challenges in Real-Time Implementation
10.2. Hardware and Software Requirements for Real-Time Filters
10.3. Real-Time Processing with MATLAB and Simulink
10.4. Adaptive Filtering in Embedded Systems
10.5. Real-Time Application Examples

Chapter 11: MATLAB for Adaptive Filtering
11.1. Introduction to MATLAB for Signal Processing
11.2. MATLAB Functions for Adaptive Filtering
11.3. Simulating Adaptive Filters in MATLAB
11.4. Creating Custom Adaptive Algorithms in MATLAB
11.5. Visualizing Filter Performance Using MATLAB
11.6. Case Studies Using MATLAB Code Examples

Chapter 12: Applications of Adaptive Filters
12.1. Adaptive Filters in Audio and Speech Processing
12.2. Adaptive Filters in Medical Signal Processing
12.3. Adaptive Filtering in Seismic and Geophysical Signals
12.4. Applications in Radar and Sonar Systems
12.5. Case Studies: Real-World Adaptive Filtering Applications

Chapter 13: Performance Analysis of Adaptive Filters
13.1. Filter Performance Metrics: Convergence and Stability
13.2. Mean Square Error (MSE) and Its Minimization
13.3. Adaptive Filter Robustness and Error Sensitivity
13.4. Trade-offs in Filter Design
13.5. MATLAB Performance Evaluation Tools

Appendices
A.1. MATLAB Code for Basic Adaptive Filters
A.2. MATLAB Simulink Models for Adaptive Filtering
A.3. Mathematical Derivations for Key Algorithms
A.4. Glossary of Terms
A.5. Index

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