Syllabus :

Statistical Signal Processing

Code: EE 522 | L-T-P-C : 3-0-0

Stationary processes, Strict sense stationarity, Wide sense stationarity and Cyclostationarity for continuous time and discrete time random processes; Discrete time Markov processes, Spectral analysis of wide sense stationary processes, Synthesis models for white noise; Signal synthesis models, Detection of signals in noisy environments, Setting up Hypotheses for varied application scenarios, Simple Hypothesis and Composite Hypothesis, - Includes both the multi-state problem as well as uncertainties in the observation model parameters; Bayesian, MIN-MAX and Neyman-Pearson; Introduction to Sufficient statistics; channel sensing, signal detection, signal bandwidth estimation; Bayesian, Maximum Likelihood (ML), Minimum Mean Square estimation (MMSE) procedures; Fisher information and Cramer-Rao Bound; Applications to Kalman filter tracking; Introduction to Adaptive filters, Least Mean Squares (LMS) algorithm, Recursive least squares (RLS).

Texts / References:

1. H. L. Van Trees, Detection, Estimation and Modulation Theory, Part I, John Wiley, 1968.
2. H. V. Poor, An Introduction to Signal Detection and Estimation, 2nd edition, Springer, 1994.
3. S. J. Orfanidis, Optimum Signal Processing, 2nd Edition, 2007 republication of the 1988 McGraw-Hill edition.