# Course Websites

3

## ECE 561 - Detection and Estimation Theory

### Spring 2021

#### Official Description

Detection and estimation theory, with applications to communication, control, and radar systems; decision-theory concepts and optimum-receiver principles; detection of random signals in noise, coherent and noncoherent detection; parameter estimation, linear and nonlinear estimation, and filtering. Course Information: Prerequisite: ECE 534.

Communications

#### 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.

TitleSectionCRNTypeHoursTimesDaysLocationInstructor
Detection & Estimation TheoryE34003OD41100 - 1220 T R    Venugopal V. Veeravalli