Mohamed (Universite Bordeaux I (France)) Najim,
M Najim
John Wiley & Sons
e druk, 2008
9781848210226
Modeling, Estimation and Optimal Filtration in Signal Processing
Specificaties
Gebonden, 408 blz.
|
Engels
John Wiley & Sons |
e druk, 2008
ISBN13: 9781848210226
Rubricering
Onderdeel van serie
ISTE
Levertijd ongeveer 15 werkdagen
Samenvatting
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.
Specificaties
ISBN13:9781848210226
Taal:Engels
Bindwijze:gebonden
Aantal pagina's:408
Uitgever:John Wiley & Sons
Serie:ISTE
Inhoudsopgave
Chapter 1. Introduction to Parametric Models.
<br />
<br /> Chapter 2. Least–Squares Estimation of Linear Model Parameters.
<br />
<br /> Chapter 3. Matched Filters and Wiener Filters.
<br />
<br /> Chapter 4. Adaptive Filters.
<br />
<br /> Chapter 5. Kalman Filters.
<br />
<br /> Chapter 6. Kalman Filtering for Speech Enhancement.
<br />
<br /> Chapter 7. Instrumental Variable Techniques.
<br />
<br /> Chapter 8. H Infinity Techniques: An Alternative to Kalman filters?
<br />
<br /> Chapter 9. Introduction to Particle Filtering.
<br />
<br /> Appendix.
<br />
<br /> Chapter 2. Least–Squares Estimation of Linear Model Parameters.
<br />
<br /> Chapter 3. Matched Filters and Wiener Filters.
<br />
<br /> Chapter 4. Adaptive Filters.
<br />
<br /> Chapter 5. Kalman Filters.
<br />
<br /> Chapter 6. Kalman Filtering for Speech Enhancement.
<br />
<br /> Chapter 7. Instrumental Variable Techniques.
<br />
<br /> Chapter 8. H Infinity Techniques: An Alternative to Kalman filters?
<br />
<br /> Chapter 9. Introduction to Particle Filtering.
<br />
<br /> Appendix.