Course Content :-

• Part I Detection Theory:Hypothesis testing: Likelihood Ratio Test, Bayes’ Criterion,  Minimax  Criterion,  Neyman-Pearson  Criterion, Sufficient Statistics, Performance Evaluation.
• Multiple hypothesis testing.
• Composite hypothesis testing.
• Sequential detection.
• Detection of known signals in white noise.
• Detection of known signals in colored noise.
• Detection of signals with unknown parameters.
• Non-parametric detection.
• Part II Estimation Theory: Bayesian parameter estimation.
• Non-Bayesian parameter estimation.
• Properties of estimators: sufficient statistics, bias, consistency, efficiency, Cramer-Rao bounds.
• Linear Mean-Square Estimation.
• Waveform Estimation.