Winter 2013

EE262: Detection and Estimation

Covers fundamental approaches to designing optimal estimators and detectors of deterministic and random parameters and processes in noise, and includes analysis of their performance. Binary hypothesis testing: the Neyman-Pearson Theorem. Receiver operating characteristics. Deterministic versus random signals. Detection with unknown parameters. Optimal estimation of the unknown parameters: least square, maximum likelihood, Bayesian estimation. The course includes review of the fundamental mathematical and statistical techniques employed. Many applications of the techniques are presented throughout the course.