ET4386 Estimation and detection
Topics: Basics of detection and estimation theory, as used in statistical signal processing, adaptive beamforming, speech enhancement, radar, telecommunication, localization, system identification, and elsewhere.
This course covers the basics of detection and estimation theory, as used in statistical signal processing, adaptive beamforming, speech enhancement, radar, telecommunication, localization, system identification, and elsewhere.
Part I: Optimal estimation covers minimum variance unbiased (MVU) estimators, the Cramer-Rao bound (CRB), best linear unbiased estimators (BLUE), maximum likelihood estimation (MLE), recursive least squares (RLE), Bayesian estimation techniques, and the 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.
For the course details, click on "More information" on the menu at the right side of the webpage.
Teachers
dr.ir. Richard Hendriks
Audio signal processing, signal processing for hearing aids, biomedical signal processing
dr. Raj Thilak Rajan
Distributed autonomous systems, Positioning Navigation Timing (PNT), Space systems
Last modified: 2020-10-22
