{"id":987495753,"date":"2024-10-29T13:33:02","date_gmt":"2024-10-29T08:03:02","guid":{"rendered":"https:\/\/testece.iisc.ac.in\/~cmurthy\/wordpress\/?page_id=987495753"},"modified":"2024-10-29T14:19:59","modified_gmt":"2024-10-29T08:49:59","slug":"e1-244-detection-and-estimation-theory","status":"publish","type":"page","link":"https:\/\/ece.iisc.ac.in\/~cmurthy\/e1-244-detection-and-estimation-theory\/","title":{"rendered":"E1 244: Detection and Estimation Theory"},"content":{"rendered":"

[et_pb_section fb_built=”1″ fullwidth=”on” _builder_version=”4.27.0″ background_image=”https:\/\/ece.iisc.ac.in\/~cmurthy\/wp-content\/uploads\/2024\/09\/GOPR0015-scaled.jpg” global_colors_info=”{}”][et_pb_fullwidth_header title=”E1 244: Detection and Estimation Theory” content_max_width=”none” module_class=”sub_header” _builder_version=”4.27.0″ title_font=”|on|||” title_text_color=”#FFFFFF” title_font_size=”50px” background_color=”rgba(255, 255, 255, 0)” background_image=”https:\/\/ece.iisc.ac.in\/~cmurthy\/wp-content\/uploads\/2024\/09\/abstract-1438441.jpg” parallax=”on” button_one_letter_spacing_hover=”0″ button_two_letter_spacing_hover=”0″ global_colors_info=”{}” button_one_text_size__hover_enabled=”off” button_two_text_size__hover_enabled=”off” button_one_text_color__hover_enabled=”off” button_two_text_color__hover_enabled=”off” button_one_border_width__hover_enabled=”off” button_two_border_width__hover_enabled=”off” button_one_border_color__hover_enabled=”off” button_two_border_color__hover_enabled=”off” button_one_border_radius__hover_enabled=”off” button_two_border_radius__hover_enabled=”off” button_one_letter_spacing__hover_enabled=”on” button_one_letter_spacing__hover=”0″ button_two_letter_spacing__hover_enabled=”on” button_two_letter_spacing__hover=”0″ button_one_bg_color__hover_enabled=”off” button_two_bg_color__hover_enabled=”off”][\/et_pb_fullwidth_header][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.27.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_row _builder_version=”4.27.0″ _module_preset=”default” custom_padding=”|||0px||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.0″ _module_preset=”default” custom_padding=”|||11px|false|false” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n

Logistics<\/h2>\n
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Instructor:<\/strong>\u00a0Chandra R. Murthy (cmurthy at ece)
Class hours:<\/strong>\u00a0MWF 11am-12:30pm, Room L5 in the Central Lecture Hall Complex (near the GATE office).
TA<\/strong>: Bharath B. N. and Nirmal Shende<\/p>\n

Textbooks:<\/strong>
1. H. V. Poor, An Introduction to Signal Detection and Estimation, 2nd Ed., Springer-Verlag, 1994.
2. H. L. Van Trees, Detection, Estimation and Modulation Theory, Parts 1 and 2, John Wiley Inter-Science.
3. E. L. Lehman, Testing Statistical Hypothesis, John Wiley, 1986.
4. M. D. Srinath, P. K. Rajasekaran and R. Vishwanathan, An Introduction to Statistical Signal Processing with Applications, Prentice-Hall, 1996.
5. To review probability: A. Papoulis, Probability, random variables, and stochastic processes, McGraw-Hill International Edition.<\/p>\n

Prerequisites:<\/strong>\u00a0Random processes (E2-202 or equivalent).<\/p>\n<\/div>\n

\u00a0<\/h3>\n

Syllabus:<\/h3>\n
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\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
S. No.<\/th>\nTopic<\/th>\nNum. Lectures<\/th>\n<\/tr>\n
1<\/td>\nEstimation theory: Bayesian, MMSE and MAP estimation<\/td>\n2<\/td>\n<\/tr>\n
2<\/td>\nFischer-Neyman factorization theorem, Rao-Blackwell theorem<\/td>\n2<\/td>\n<\/tr>\n
3<\/td>\nMaximum-Likelihood estimation<\/td>\n2<\/td>\n<\/tr>\n
4<\/td>\nExponential families and the Cramer-Rao Bound<\/td>\n2<\/td>\n<\/tr>\n
5<\/td>\nConsistency, efficiency and asymptotics<\/td>\n2<\/td>\n<\/tr>\n
6<\/td>\nKalman \ufb01ltering<\/td>\n2<\/td>\n<\/tr>\n
7<\/td>\nLinear estimation of signals, Weiner \ufb01ltering<\/td>\n2<\/td>\n<\/tr>\n
8<\/td>\nBayesian, Neyman-Pearson & Minimax detection<\/td>\n2<\/td>\n<\/tr>\n
9<\/td>\nComposite hypothesis testing, Generalized LRT<\/td>\n2<\/td>\n<\/tr>\n
10<\/td>\nSequential and distributed detection<\/td>\n4<\/td>\n<\/tr>\n
11<\/td>\nPerformance analysis<\/td>\n2<\/td>\n<\/tr>\n
12<\/td>\nSignal detection in continuous time, KL theorem<\/td>\n2<\/td>\n<\/tr>\n
13<\/td>\nDetection in Gaussian noise<\/td>\n2<\/td>\n<\/tr>\n
\u00a0<\/td>\nTotal<\/strong><\/td>\n28<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n

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Grading<\/h2>\n
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Midterm 1: Feb. 11, 2011, in class: 25%. For solutions, click\u00a0here<\/a>.
Midterm 2: Mar. 14, 2011, in class: 25%. For solutions, click\u00a0here<\/a>.
Final:\u00a0Apr. 26, 2011, 9am – 12pm: 50%<\/strong><\/p>\n<\/div>\n

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Homeworks<\/h2>\n
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Homeworks are posted\u00a0here<\/a>.<\/p>\n<\/div>\n

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Announcements<\/h3>\n
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