ET4386 Estimation and detection

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 estimators, the Cramer-Rao bound, best linear unbiased estimators, maximum likelihood estimation, recursive least squares, Bayesian estimation techniques, and the Wiener filter.. 

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.

This course gives a solid background for EE4715 Array Processing and EE4685 Machine Learning, a Bayesian Perspective.

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Last modified: 2024-11-05