*****COURSES ARE SUBJECT TO CHANGE*****
Covers fundamental approaches to designing optimal estimators and detectors of deterministic and random parameters and processes in noise, and includes analysis of their performance. Binary hypothesis testing: the Neyman-Pearson Theorem. Receiver operating characteristics. Deterministic versus random signals. Detection with unknown parameters. Optimal estimation of the unknown parameters: least square, maximum likelihood, Bayesian estimation. Will review the fundamental mathematical and statistical techniques employed. Many applications of the techniques are presented throughout the course. Note: While a review of probability and statistics is provided, this is not a basic course on this material. (Formerly Statistical Signal Processing I.) Prerequisite(s): course 103 and Computer Engineering 107, or permission of instructor.
5 Credits
Year | Fall | Winter | Spring | Summer |
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2018-19 |
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2017-18 |
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2016-17 |
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2015-16 |
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2014-15 |
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2013-14 |
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2012-13 |
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2011-12 |
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2010-11 |
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2009-10 |
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2008-09 |
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2007-08 |
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2006-07 |
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2005-06 |
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2004-05 |
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2003-04 |
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