
E1 244 Detection and EstimationAbout E1 244 Detection and EstimationThe main goal of E1 244 is to cover the two major domains of statistical signal processing, namely, detection and estimation, which include the many mathematical tools that engineers and statisticians use to draw inference from imperfect or incomplete measurements. The first part of the course develops statistical parameter estimation methods to extract information from signals in noise. The second part of this course is about the application of statistical hypothesis testing to the detection of signals in noise. PrerequisiteMatrix theory (or equivalent) and Random processes (or equivalent). Teaching Assistant
SyllabusReview of linear algebra and random processes. Maximum likelihood theory, minimum variance unbiased estimators, the CramÃ©rRao bound, best linear unbiased estimators, least squares and recursive least squares, Bayesian estimation techniques, the Wiener and Kalman filters, binary and multiple hypothesis testing, NeymanPearson detector, Bayes detector, composite hypothesis testing with unknown signal and noise parameters, and sequential probability ratio test. Textbooks
Course requirements and grading
Schedule (Schedule 2022)
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