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

dr.ir. Sundeep Prabhakar Chepuri

Signal processing; Sparse Sampling; Statistical inference.

Last modified: 2016-09-16

Details

Credits: 5 EC
Period: 0/4/0/0
Contact: Richard Hendriks