ECE 561
Detection and Estimation Theory

Displaying course information from Spring 2014.

Section Type Times Days Location Instructor
E DIS 0930 - 1050 T R   1111 Siebel Center for Comp Sci  Venugopal Veeravalli
Web Page
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.
Subject Area Communications
Course Prerequisites Credit in ECE 534
Course Directors Pierre Moulin
Venugopal Varadachari Veeravalli
Detailed Description and Outline


  • 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
H.V. Poor, An Introduction to Signal Detection and Estimation, Springer-Verlag, 1994.
Last updated: 2/13/2013