Course Websites
ECE 561 - Detection and Estimation Theory
Last offered Spring 2024
Official Description
Fundamental principles of statistical decision theory and their application to hypothesis testing and estimation; classical optimality criteria for decision rules; computationally efficient implementations; sequential decision-making; performance analysis; asymptotic properties and performance of decision rules. Course Information: 4 graduate hours. No professional credit. Prerequisite: ECE 534.
Related Faculty
Subject Area
- Communications
Course Director
Description
Introduction to detection and estimation theory, with applications to communication, control, and signal processing; decision-theory concepts and optimum-receiver principles; detection of random signals in noise; and parameter estimation, linear and nonlinear estimation, and filtering.
Topics
- Introduction
- Basic concepts of statistical decision theory: Main ingredients; concepts of optimality (Bayesian and minimax approaches)
- Binary hypothesis testing: Bayesian decision rules; minimax decision rules; Neyman-Pearson decision rules (the radar problem); composite hypothesis testing
- Signal detection in discrete time: models and detector structures; performance evaluation; Chernoff bounds and large deviations; sequential detection, quickest change detection, robust detection
- Parameter estimation: Bayesian estimation; nonrandom parameter estimation; maximum likelihood estimation, robust estimation
- Signal estimation in discrete time: Kalman filter; recursive Bayesian and ML estimation
Detailed Description and Outline
Topics:
- Introduction
- Basic concepts of statistical decision theory: Main ingredients; concepts of optimality (Bayesian and minimax approaches)
- Binary hypothesis testing: Bayesian decision rules; minimax decision rules; Neyman-Pearson decision rules (the radar problem); composite hypothesis testing
- Signal detection in discrete time: models and detector structures; performance evaluation; Chernoff bounds and large deviations; sequential detection, quickest change detection, robust detection
- Parameter estimation: Bayesian estimation; nonrandom parameter estimation; maximum likelihood estimation, robust estimation
- Signal estimation in discrete time: Kalman filter; recursive Bayesian and ML estimation
Texts
P. Moulin and V.V. Veeravalli, Statistical Inference for Engineers and Data Scientists, Cambridge University Press, 2019.
| Title | Section | CRN | Type | Hours | Times | Days | Location | Instructor |
|---|---|---|---|---|---|---|---|---|
| Statistical Inference ENG & DS | E | 34003 | DIS | 4 | 1100 - 1220 | T R | 2013 Electrical & Computer Eng Bldg | Venugopal V. Veeravalli |