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ENEE 621 - Detection and Estimation Theory Credits: [3]
Description: Fundamentals of detection and estimation theory for statistical signal processing applications; theory of hypothesis testing (binary, multiple and composite hypotheses and Bayesian, Neyman Pearson and minimax approaches); theory of signal detection (discrete and continuous time signals; deterministic and random signals; white Gaussian noise, general independent noise and special classes of dependent noise, e.g. colored Gaussian noise, signal design and representations); theory of signal parameter estimation; minimum variance unbiased (MVU) estimation; Cramer-Rao lower bound; general MVU estimation, linear models; maximum likelihood estimation, least squares; general Bayesian estimators (minimum mean-square error and maximum a posterior estimators); linear Bayesian estimators (Wiener filters) and Kalman filters.
Course ID: 053936
Prerequisite: Prerequisite: ENEE 620 or consent of instructor.
Components: Lecture
Grading Method: A-F, Pass/Fail, Audit
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