ECE 561 - Detection and Estimation Theory

Semesters Offered

TitleRubricSectionCRNTypeTimesDaysLocationInstructor
Detection & Estimation TheoryECE561E34003DIS1100 - 1220 T R  2013 ECE Building Venugopal Veeravalli

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.

Prerequisites

Credit in ECE 534

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

H.V. Poor, An Introduction to Signal Detection and Estimation, Springer-Verlag, 1994.

Last updated

2/13/2013