Modeling, estimation and optimal filtration in signal processing / Mohamed Najim.

Format:

Book

Author/Creator:

Najim, Mohamed.

Series:

ISTE

ISTE ; v.25

Standardized Title:

Modelisation, estimation et filtrage optimal en traitement du signal. English

Language:

English

Subjects (All):

Electric filters, Digital.

Signal processing--Digital techniques.

Signal processing.

Physical Description:

1 online resource (410 p.)

Edition:

1st edition

Place of Publication:

London : ISTE ; Hoboken, NJ : J. Wiley & Sons, 2008.

Language Note:

English

System Details:

text file

Summary:

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 the

Contents:

Modeling, Estimation and Optimal Filtering in Signal Processing; Table of Contents; Preface; Chapter 1. Parametric Models; 1.1. Introduction; 1.2. Discrete linear models; 1.2.1. The moving average (MA) model; 1.2.2. The autoregressive (AR) model; 1.3. Observations on stability, stationarity and invertibility; 1.3.1. AR model case; 1.3.2. ARMA model case; 1.4. The AR model or the ARMA model?; 1.5. Sinusoidal models; 1.5.1. The relevance of the sinusoidal model; 1.5.2. Sinusoidal models; 1.6. State space representations; 1.6.1. Definitions

1.6.2. State space representations based on differential equation representation1.6.3. Resolution of the state equations; 1.6.4. State equations for a discrete-time system; 1.6.5. Some properties of systems described in the state space; 1.6.5.1. Introduction; 1.6.5.2. Observability; 1.6.5.3. Controllability; 1.6.5.4. Plurality of the state space representation of the system; 1.6.6. Case 1: state space representation of AR processes; 1.6.7. Case 2: state space representation of MA processes; 1.6.8. Case 3: state space representation of ARMA processes

1.6.9. Case 4: state space representation of a noisy process1.6.9.1. An AR process disturbed by a white noise; 1.6.9.2. AR process disturbed by colored noise itself modeled by another AR process; 1.6.9.3. AR process disturbed by colored noise itself modeled by a MA process; 1.7. Conclusion; 1.8. References; Chapter 2. Least Squares Estimation of Parameters of Linear Models; 2.1. Introduction; 2.2. Least squares estimation of AR parameters; 2.2.1. Determination or estimation of parameters?; 2.2.2. Recursive estimation of parameters; 2.2.3. Implementation of the least squares algorithm

2.2.4. The least squares method with weighting factor2.2.5. A recursive weighted least squares estimator; 2.2.6. Observations on some variants of the least squares method; 2.2.6.1. The autocorrelation method; 2.2.6.2. Levinson's algorithm; 2.2.6.3. The Durbin-Levinson algorithm; 2.2.6.4. Lattice filters; 2.2.6.5. The covariance method; 2.2.6.6. Relation between the covariance method and the least squares method; 2.2.6.7. Effect of a white additive noise on the estimation of AR parameters; 2.2.6.8. A method for alleviating the bias on the estimation of the AR parameters

2.2.7. Generalized least squares method2.2.8. The extended least squares method; 2.3. Selecting the order of the models; 2.4. References; Chapter 3. Matched and Wiener Filters; 3.1. Introduction; 3.2. Matched filter; 3.2.1. Introduction; 3.2.2. Matched filter for the case of white noise; 3.2.3. Matched filter for the case of colored noise; 3.2.3.1. Formulation of problem; 3.2.3.2. Physically unrealizable matched filter; 3.2.3.3. A matched filter solution using whitening techniques; 3.3. The Wiener filter; 3.3.1. Introduction; 3.3.2. Formulation of problem; 3.3.3. The Wiener-Hopf equation

3.3.4. Error calculation in a continuous physically non-realizable Wiener filter

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

ISBN:

9786612165009

9781282165007

1282165003

9780470611104

0470611103

9780470393680

0470393688

OCLC:

520990432