Course Websites

ECE 561 - Detection and Estimation Theory

Last offered Spring 2022

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

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
Statistical Inference ENG & DSE34003DIS41100 - 1220 T R  2013 Electrical & Computer Eng Bldg Pierre Moulin