Review of Gaussian Variables and Processes: Problem Formulation and Objective of Signal Detection and Signal Parameter Estimation in Discrete-Time Domain, Estimation of Signal Parameters: Minimum Variance Unbiased Estimation(MVUE), Fisher Information Matrix, Cramer-Rao Lower Bound, Linear Models, Generalized Minimum Variance Unbiased Estimation, Best Linear Unbiased Estimators (BLUE) Maximum Likelihood Estimation(MLE), Statistical Decision Theory: Neyman-Pearson Theorem, Receiver Operating Characteristics, Irrelevant Data, Minimum Probability of Error, Bayes Risk, Multiple Hypothesis Testing, Detection of Deterministic Signals: Matched Filter Detector and Its Performance Generalized Matched Filter Multiple Signals. Detection of Random Signals: Estimator-Correlator, Linear Model, General Gaussian Detection, Composite Hypothesis Testing and it’s approaches, Locally Most Powerful Detectors, Multiple Hypothesis Testings.

To build concepts on statistical signal processing

Students will have concepts for research on Digital Signal Processing, Communication, Information Theory, Detection Theory, Array Signal Processing, Radar, Sonar, Acoustic Signal Processing

S. M. Kay, Fundamentals of Statistical Signal Processing: Estimation Theory, Printice hall , Vol I 2009

S. M. Kay, Fundamentals of Statistical Signal Processing: Detection Theory, Printice hall , Vol II 2008

H V Poor, An Introduction to Signal Detection and Estimation, Springer , 2nd Edition 2008

Harry L. Van Trees, Detection, Estimation and Modulation Theory, John Wiley & Sons , Part I 2002