05422nam 2200673Ia 450 991087693470332120200520144314.01-282-16500-397866121650090-470-61110-30-470-39368-8(CKB)2550000000005902(EBL)477691(SSID)ssj0000340485(PQKBManifestationID)11947674(PQKBTitleCode)TC0000340485(PQKBWorkID)10387495(PQKB)10343192(MiAaPQ)EBC477691(CaSebORM)9781848210226(OCoLC)520990432(OCoLC)857716828(OCoLC)ocn857716828(EXLCZ)99255000000000590220071106d2008 uy 0engur|n|---|||||txtccrModeling, estimation and optimal filtration in signal processing /Mohamed Najim1st editionLondon ISTE ;Hoboken, NJ J. Wiley & Sons20081 online resource (410 p.)ISTE ;v.25Description based upon print version of record.1-84821-022-1 Includes bibliographical references and index.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. Definitions1.6.2. 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 processes1.6.9. 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 algorithm2.2.4. 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 parameters2.2.7. 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 equation3.3.4. Error calculation in a continuous physically non-realizable Wiener filterThe 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 theISTEElectric filters, DigitalSignal processingDigital techniquesElectric filters, Digital.Signal processingDigital techniques.621.382/2Najim Mohamed856048MiAaPQMiAaPQMiAaPQBOOK9910876934703321Modeling, estimation and optimal filtration in signal processing4204391UNINA