Modeling, Estimation and Optimal Filtration in Signal Processing / Edition 1

Modeling, Estimation and Optimal Filtration in Signal Processing / Edition 1

by Mohamed Najim
     
 

The purpose of this book is to provide graduate students and practitioners with traditional methods and more recent results for model-based approaches in signal processing.
Firstly, discrete-time linear models such as AR, MA and ARMA models, their properties and their limitations are introduced. In addition, sinusoidal models are addressed.
Secondly,

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Overview

The purpose of this book is to provide graduate students and practitioners with traditional methods and more recent results for model-based approaches in signal processing.
Firstly, discrete-time linear models such as AR, MA and ARMA models, their properties and their limitations are introduced. In addition, sinusoidal models are addressed.
Secondly, estimation approaches based on least squares methods and instrumental variable techniques are presented.
Finally, the book deals with optimal filters, i.e. Wiener and Kalman filtering, and adaptive filters such as the RLS, the LMS and their variants.

Product Details

ISBN-13:
9781848210226
Publisher:
Wiley
Publication date:
06/30/2008
Series:
ISTE Series, #302
Pages:
400
Product dimensions:
6.00(w) x 9.30(h) x 1.10(d)

Table of Contents

Chapter 1. Introduction to Parametric Models.

Chapter 2. Least-Squares Estimation of Linear Model Parameters.

Chapter 3. Matched Filters and Wiener Filters.

Chapter 4. Adaptive Filters.

Chapter 5. Kalman Filters.

Chapter 6. Kalman Filtering for Speech Enhancement.

Chapter 7.  Instrumental Variable Techniques.

Chapter 8.  H Infinity Techniques: An Alternative to Kalman filters?

Chapter 9.  Introduction to Particle Filtering.

Appendix.

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