Course Title:
Detection and Estimation Theory
Course Description:
Reviews vector space and stochastic concepts, sufficiency, unbiased estimation, Cramer-Rao bound, Rao-Blackwell theorem, Pitman efficiency, maximum likelihood estimation, Bayesian estimation, minimum mean squared error estimation, least squares estimation, and Gauss-Markov theorem. Topics include simple and composite hypotheses, Neyman-Pearson tests, uniformly most powerful tests, invariant tests, CFAR detection, Bayesian detection, minimax detection, nonparametric testing, sequential testing, and quickest detection.
Fall Offering:
Lab/Coreq 1:
Spring Offering:
Lab/Coreq 2:
Summer Offering:
Lab/Coreq Remarks:
Summer 1 Offering:
Prerequisite 1:
ECE G204
Summer 2 Offering:
Prerequisite 2:
Cross-Listed Course 1:
Prerequisite 3:
Cross-Listed Course 2:
Prerequisite 4:
Cross-Listed Course 3:
Prerequisite 5:
Cross-Listed Course 4:
Prerequisite Remarks:
Or permission of instructor/faculty.
Cross-Listed Course 5:
Repeatable:
N
