ECE 561 - Detection and Estimation Theory

Summer 2009 | Fall 2009 | Spring 2010 | Summer 2010
Web Page http://courses.ece.uiuc.edu/ece561/
Subject Area Communications
Course Prerequisites Credit in ECE 534
Course Directors Venugopal Varadachari Veeravalli
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.
Credit 4 hours
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
Course Prerequisites ECE 534
Texts H.V. Poor, An Introduction to Signal Detection and Estimation, Springer-Verlag, 1994.