Estimation and Detection

This course covers the basics of detection and estimation theory, as used in statistical signal processing, adaptive beamforming, radar, biomedical, telecommunication, system identification, and elsewhere.

Part I: Optimal estimation covers minimum variance unbiased estimators, the Cramer-Rao bound, best linear unbiased estimators, maximum likelihood estimation, least squares, Bayesian estimation techniques including Wiener and Kalman filters.

Part II: Detection theory covers simple and multiple hypothesis testing, the Neyman-Pearson Theorem, Bayes Risk, and testing with unknown signal and noise parameters.