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Detection and Estimation Theory Lab


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Detection and Estimation Theory Simulation Trainer


1 Unit I : Statistical Decision Theory
Introduction, Bayes’ Criterion-Binary Hypothesis Testing, M-ary Hypothesis Testing, Minimax Criterion, Neyman-Pearson Criterion, Composite Hypothesis Testing, Sequential Detection.
2 Unit II : Parameter Estimation-I
Introduction, Some Criteria for Good Estimators, Maximum Likelihood Estimation, Generalized Likelihood Ratio Test, Bayes’ Estimation
3 Unit III : Parameter Estimation-II
Cramer-Rao Inequality, Multiple Parameter Estimation, Best Linear Unbiased Estimator, Least-Square Estimation, Recursive Least-Square Estimator.
4 Unit IV : Filtering
Introduction, Linear Transformation and Orthogonality Principle, Wiener Filters, Discrete Wiener Filters, Kalman Filter.
5 Unit V : Detection and Parameter Estimation
Introduction, Signal Representation, Binary Detection, M-ary Detection, Linear Estimation.
6 Unit VI : Detection Theory in Radar
Introduction, Radar Elementary concepts- Range, Range Resolution, and Unambiguous Range, Doppler Shift, Principles of Adaptive CFAR Detection- Target Models, Review of Some CFAR Detectors.


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