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Course Syllabus 18-752 A & SV: Spring, 2014

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Course Syllabus 18-752 A & SV: Spring, 2014
Course Syllabus
18-752 A & SV: Estimation, Detection and Identification
Spring, 2014
Instructor: Osman Yagan
Office Location: B 23 121B
Phone: 650-335-2822
Email Address: [email protected]
Office Hours: Thursday 3:30PM – 4:30 PM, B 23 121B
Teaching Assistant(s): Serim Park
Email Address: [email protected]
Office Hours: Wednesday 4PM - 6PM, HH c120
Academic Services Assistant: Shannon Lown
Email Address: [email protected]
Office Location: HH 1112
Course Description:
Decision theory: Binary hypothesis testing, M-ary testing, Bayes, Neyman-Pearson,
Min-Max. Performance. Probability of error, ROC. Estimation theory: linear and
nonlinear estimation, parameter estimation. Bayes, MAP, maximum likelihood, CramérRao bounds. Bias, efficiency, consistency. Asymptotic properties of estimators.
Orthogonal decomposition of random processes and harmonic representation.
Waveform detection and estimation. Wiener filtering and Kalman-Bucy filtering.
Elements of identification. Recursive algorithms. Spectral estimation. Topics may vary.
Number of Units: 12
Pre-requisites: 18-751 and senior or graduate standing.
Course Area: Signals and Systems, Signal Processing and Communications
Class Schedule
• Lecture:
Section A: Tuesday & Thursday 4:30PM -6:20PM WEH 5328
Section SV: Tuesday & Thursday 1:30PM – 3:20PM B23 211
Required Textbook: Statistical Signal Processing, Detection, Estimation, and Time
Series Analysis by Louis L Scharf
Suggested Reading: Detection, Estimation, and Modulation Theory Part I, by Harry Van
Trees
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Brief List of Topics Covered:
Detection: Binary and M-ary hypothesis testing. Neyman-Pearson detection. Invariance.
Matched filter, CFAR matched filter and variants. Bayes detection. Likelihood ratios.
Estimation: Maximum-likelihood estimation. Bayes estimation. Sufficiency and
invariance. Cramer-Rao bounds. Estimation with the linear statistical model. Minimum
mean square error. Recursive estimation. Kalman-Bucy filter.
Identification: ML identification of ARMA models, signal subspaces, parameters in
sinusoidal models.
Homeworks:
Homeworks will be handed out roughly one per week, and due the next week.
Homeworks will be graded only qualitatively on a 2, 1, or zero scale. No late homeworks
please! Please see the Academic Integrity policy below on what constitutes acceptable
cooperation and help in doing homeworks. This policy is strictly enforced.
Exams:
There will be two exams, a mid-semester and a final. Tentative exam schedules:
Exam 1: February 25th, in class
Exam 2: May 1st, in class.
Project:
There is a course project, due at the end of the semester. This will be in the form of an
oral presentation, as well as a written report.
Course Blackboard: To access the course blackboard from an Andrew Machine, go to
the login page at: http://www.cmu.edu/blackboard. You should check the course
blackboard daily for announcements and handouts.
Course Wiki:
Students are encouraged to use the ECE wiki to provide feedback about the course at:
http://wiki.ece.cmu.edu/index.php.
Grading Algorithm:
Each of the two exams as well as the course project contribute 25 % of the final score, for
a total of 75%. The homeworks contribute the remaining 25%. Letter grades will be
derived from the final numerical score. The following is a (tentative) guideline to convert
numerical scores (NN) to letter grades (Grade):
Grade=A if NN ≥ 80 %
Grade=B if 65 % ≤ NN < 80 %
Grade=C if 50 % ≤ NN < 65 %
Grade=D if 40 % ≤ NN < 50 %
Grade=F if NN < 40 %
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Tentative Course Calendar
Date
Day
January
14
Tues.
16
Thurs.
21
Tues.
23
Thurs.
28
Tues.
30
Thurs.
February
4
Tues.
6
Thurs.
11
Tues.
13
Thurs.
18
Tues.
20
Thurs.
25
Tues.
27
Thurs.
March
4
Tues.
6
Thurs.
11
Tues.
13
Thurs.
18
Tues.
20
Thurs.
25
Tues.
27
Thurs.
April
1
Tues.
3
Thurs.
8
Tues.
10
Thurs.
15
Tues.
17
Thurs.
22
Tues.
24
Thurs.
29
Tues.
May
1
Thurs.
6
Tues.
8
Thurs.
Class Activity
Mid-semester Test
SPRING BREAK (NO CLASSES)
SPRING BREAK (NO CLASSES)
No Classes
Project presentations
Project presentations
Final Test
Final Exam Period
Final Exam Period
Education Objectives (Relationship of Course to Program Outcomes)
(a) an ability to apply knowledge of mathematics, science, and engineering:
The course uses probability theory and linear algebra to develop statistical estimation and
detection techniques for engineering problems.
(e) an ability to identify, formulate, and solve engineering problems:
The homeworks, exams and project are based on statistical techniques to identify and
solve engineering problems.
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(g) an ability to communicate effectively:
A course project has an oral presentation to summarize the project effort.
(j) a knowledge of contemporary issues:
A course project will allow students to be exposed to contemporary research on
estimation and detection methods.
(k) an ability to use the techniques, skills, and modern engineering tools necessary for
engineering practice:
The homeworks, exams and project are based on statistical techniques to identify and
solve engineering problems.
Academic Integrity Policy (http://www.ece.cmu.edu/student/integrity.html):
The Department of Electrical and Computer Engineering adheres to the academic
integrity policies set forth by Carnegie Mellon University and by the College of
Engineering. ECE students should review fully and carefully Carnegie Mellon
University's policies regarding Cheating and Plagiarism; Undergraduate Academic
Discipline; and Graduate Academic Discipline. ECE graduate student should further
review the Penalties for Graduate Student Academic Integrity Violations in CIT outlined
in the CIT Policy on Graduate Student Academic Integrity Violations. In addition to the
above university and college-level policies, it is ECE's policy that an ECE graduate
student may not drop a course in which a disciplinary action is assessed or pending
without the course instructor's explicit approval. Further, an ECE course instructor may
set his/her own course-specific academic integrity policies that do not conflict with
university and college-level policies; course-specific policies should be made available to
the students in writing in the first week of class.
This policy applies, in all respects, to this course.
Carnegie Mellon University's Policy on Cheating and Plagiarism
(http://www.cmu.edu/policies/documents/Cheating.html) states the following,
Students at Carnegie Mellon are engaged in preparation for professional activity of the
highest standards. Each profession constrains its members with both ethical
responsibilities and disciplinary limits. To assure the validity of the learning experience a
university establishes clear standards for student work.
In any presentation, creative, artistic, or research, it is the ethical responsibility of each
student to identify the conceptual sources of the work submitted. Failure to do so is
dishonest and is the basis for a charge of cheating or plagiarism, which is subject to
disciplinary action.
Cheating includes but is not necessarily limited to:
1. Plagiarism, explained below.
2. Submission of work that is not the student's own for papers, assignments or exams.
3. Submission or use of falsified data.
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4. Theft of or unauthorized access to an exam.
5. Use of an alternate, stand-in or proxy during an examination.
6. Use of unauthorized material including textbooks, notes or computer programs in the
preparation of an assignment or during an examination.
7. Supplying or communicating in any way unauthorized information to another student
for the preparation of an assignment or during an examination.
8. Collaboration in the preparation of an assignment. Unless specifically permitted or
required by the instructor, collaboration will usually be viewed by the university as
cheating. Each student, therefore, is responsible for understanding the policies of the
department offering any course as they refer to the amount of help and collaboration
permitted in preparation of assignments.
9. Submission of the same work for credit in two courses without obtaining the
permission of the instructors beforehand.
Plagiarism includes, but is not limited to, failure to indicate the source with quotation
marks or footnotes where appropriate if any of the following are reproduced in the work
submitted by a student:
1. A phrase, written or musical.
2. A graphic element.
3. A proof.
4. Specific language.
5. An idea derived from the work, published or unpublished, of another person.
This policy applies, in all respects, to 18-752 A & SV.
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