LEADER 05439nam 2200673Ia 450 001 9910139493903321 005 20170815144646.0 010 $a1-282-16500-3 010 $a9786612165009 010 $a0-470-61110-3 010 $a0-470-39368-8 035 $a(CKB)2550000000005902 035 $a(EBL)477691 035 $a(OCoLC)593301449 035 $a(SSID)ssj0000340485 035 $a(PQKBManifestationID)11947674 035 $a(PQKBTitleCode)TC0000340485 035 $a(PQKBWorkID)10387495 035 $a(PQKB)10343192 035 $a(MiAaPQ)EBC477691 035 $a(CaSebORM)9781848210226 035 $a(EXLCZ)992550000000005902 100 $a20071106d2008 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aModeling, estimation and optimal filtration in signal processing$b[electronic resource] /$fMohamed Najim 205 $a1st edition 210 $aLondon $cISTE ;$aHoboken, NJ $cJ. Wiley & Sons$d2008 215 $a1 online resource (410 p.) 225 1 $aISTE ;$vv.25 300 $aDescription based upon print version of record. 311 $a1-84821-022-1 320 $aIncludes bibliographical references and index. 327 $aModeling, 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 327 $a1.6.2. 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 327 $a1.6.9. 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 327 $a2.2.4. 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 327 $a2.2.7. 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 327 $a3.3.4. Error calculation in a continuous physically non-realizable Wiener filter 330 $aThe purpose of this book is to provide graduate students and practitioners with traditional methods and more recent results for model-based approaches in signal processing.Firstly, discrete-time linear models such as AR, MA and ARMA models, their properties and their limitations are introduced. In addition, sinusoidal models are addressed.Secondly, estimation approaches based on least squares methods and instrumental variable techniques are presented.Finally, the book deals with optimal filters, i.e. Wiener and Kalman filtering, and adaptive filters such as the RLS, the LMS and the 410 0$aISTE 606 $aElectric filters, Digital 606 $aSignal processing$xDigital techniques 608 $aElectronic books. 615 0$aElectric filters, Digital. 615 0$aSignal processing$xDigital techniques. 676 $a621.382/2 676 $a621.3822 700 $aNajim$b Mohamed$0856048 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910139493903321 996 $aModeling, estimation and optimal filtration in signal processing$91911267 997 $aUNINA