Review of sampling theory. Sampling rate conversion by integer and rational factors. Efficient realization
and applications of sampling rate conversion.
Wiener filtering. Optimum linear prediction. Levinson- Durbin algorithm. Prediction error filters.
Adaptive filters. FIR adaptive LMS algorithm. Convergence of adaptive algorithms. Fast algorithms. Applications: Noise canceller, echo canceller and equalizer.
Recursive least squares algorithms. Matrix inversion lemma. Convergence analysis of the RLS algorithm. Adaptive beam forming. Kalman filtering.
Spectrum estimation. Estimation of autocorrelation. Periodogram method. Nonparametric methods. Parametric methods.
1. J.G.Proakis, M. Salehi, “Advanced Digital Signal Processing”, McGraw –Hill,1992.
2. S.Haykin, “Adaptive Filter Theory (3/e)”, Prentice- Hall,1996.
1. D.G.Manolakis, V. K. Ingle, and S. M. Kogon ,”Statistical and Adaptive Signal Processing”,
McGraw-Hill,2005
2. S.L.Marple,”Digital Spectral Analysis”,1987.
3. M.H.Hays,” Statistical Digital Signal Processing and Modeling”, John-Wiley,2001.
Students are able to
CO1: summarize multirate DSP and design efficient digital filters. CO2: construct multi-channel filter banks.
CO3: select linear filtering techniques to engineering problems. CO4: describe the most important adaptive filter generic problems.
CO5: describe the various adaptive filter algorithms.
CO6: describe the statistical properties of the conventional spectral estimators.
