Subject Code: EC6L005	Name: Statistical Signal Processing	L-T-P: 3-0-0	Credit: 3
Review of Probability and Stochastic Process Estimation Theory: Minimum-variance unbiased estimator (MVUE), Cramer-Rao Lower bound, Best Linear Unbiased Estimator, Maximum likelihood Estimator, General Bayesian Estimator Detection Theory: Neyman Pearson Theorem, Receiver Operating Characteristics, Matched Filters, Composite Hypothesis Testing Nonparametric Spectral Estimation: Estimation of power spectrum of stationary random signal using periodogram-various methods, Joint signal analysis and estimation of cross power spectrum Linear Signal Model: Synthesis of coloring filter and Analysis of whitening filter, Rational power spectra (AR, MA, ARMA), Relationship between filter parameters and autocorrelation sequences, Lattice-Ladder filter realization Parametric Spectral Estimation: Order selection criterion of AR model , Minimum-variance, Maximum entropy and Maximum likelihood spectrum estimation Harmonic models and frequency estimation techniques Harmonic Decomposition, MUSIC algorithm, ESPRIT algorithm Linear Optimum Filter: Optimum FIR Filter, PCA of optimum linear estimator and its frequency domain interpretation, Forward and Backward Linear prediction and optimum reflection coefficients Optimum causal and non-causal IIR Filters, Deconvolution and Signal restoration Algorithms and Structure of Optimum Linear Filters Levinson Recursion for optimum estimate, Order-recursive algorithms for optimum FIR filters and its lattice structures. Prerequisite: None Texts Books: Steven Kay, Fundamentals of Statistical Signal Processing, Vol I: EstimationTheory, Vol II: Detection Theory, Prentice Hall, 1993/1998. Reference Books: Harry L. Van Trees, Detection, Estimation, and Modulation Theory, Part I, Wiley-Inter science, 2001 Monson H. Hayes, Statistical Digital Signal Processing and Modeling, John Wiley, 1996.

Subject Code: EC6L005

Name: Statistical Signal Processing

L-T-P: 3-0-0

Credit: 3

Review of Probability and Stochastic Process Estimation Theory: Minimum-variance unbiased estimator (MVUE), Cramer-Rao Lower bound, Best Linear Unbiased Estimator, Maximum likelihood Estimator, General Bayesian Estimator

Detection Theory: Neyman Pearson Theorem, Receiver Operating Characteristics, Matched Filters, Composite Hypothesis Testing

Nonparametric Spectral Estimation: Estimation of power spectrum of stationary random signal using periodogram-various methods, Joint signal analysis and estimation of cross power spectrum Linear Signal Model: Synthesis of coloring filter and Analysis of whitening filter, Rational power spectra (AR, MA, ARMA), Relationship between filter parameters and autocorrelation sequences, Lattice-Ladder filter realization

Parametric Spectral Estimation: Order selection criterion of AR model , Minimum-variance, Maximum entropy and Maximum likelihood spectrum estimation Harmonic models and frequency estimation techniques Harmonic Decomposition, MUSIC algorithm, ESPRIT algorithm

Linear Optimum Filter: Optimum FIR Filter, PCA of optimum linear estimator and its frequency domain interpretation, Forward and Backward Linear prediction and optimum reflection coefficients Optimum causal and non-causal IIR Filters, Deconvolution and Signal restoration Algorithms and Structure of Optimum Linear Filters Levinson Recursion for optimum estimate, Order-recursive algorithms for optimum FIR filters and its lattice structures.

Prerequisite: None

Texts Books:

Steven Kay, Fundamentals of Statistical Signal Processing, Vol I: EstimationTheory, Vol II: Detection Theory, Prentice Hall, 1993/1998.

Reference Books:

Harry L. Van Trees, Detection, Estimation, and Modulation Theory, Part I, Wiley-Inter science, 2001
Monson H. Hayes, Statistical Digital Signal Processing and Modeling, John Wiley, 1996.