525.728.81 - Detection & Estimation Theory

Expanded Course Description

Both hypothesis testing and estimation theory are covered in this course. The course starts with an overview of the topic. A review ofprobability distributions, multivariate Gaussians, and the central limit theorem is available for review. The first half of the course is dedicated to Estimation Theory. Thus, classical approaches such as maximum likelihood estimation and Bayesian methods are discussed. Detection Theory is synonymous with Hypothesis Testing and is addressed in the second half of the semester. Topics include simple and composite hypotheses and binary and multiple hypotheses. Homework Problem Exercises and Laboratory Exercises in MATLAB are used to re-enforce class concepts. Practical problems in radar, landmine detection, and communications systems are used as examples throughout the course. Prerequisites: Mastery of Probability and Stochastic Processes (525.414 or equivalent) and a working knowledge of Signal Processing.

Course Structure

The course content is divided into modules. Course Modules can be accessed from “Course Content”. A module will have three sections: Overview, Content, and Assignments. Students are encouraged to preview all sections of the module before starting. Most modules run for a period of seven days. The student is referred to the Course Outline page for any exceptions. Students should regularly check the Course Calendar and Course Announcements for specific assignment information that may arise.

Course Topics

See Course Schedule

Course Goals

From a set of observations drawn from a random, possibly unknown distribution the student shall identify an appropriate Probability Density Function, estimate statistical parameters of that PDF, and then build an optimaldetector for deciding whether incoming observations are noise only or noise plus signal.

Course Learning Outcomes (CLOs)

• Identify observations (measurements) that are being used to determine the statistical properties of noise and signals.
• Develop and compare estimation algorithms for determining the statistical parameters of noise only PDFs and noise plus signal PDFs.
• Recognize the appropriate method for developing an optimal detector.
• Analyze the performance of various estimators using the Kramer-Rao Lower Bound criteria.
• Analyze the performance of various detection algorithms using ROC Curve analysis.

Textbooks

Required

Steven M. Kay, Fundamentals of Statistical Signal Processing Vol. 1 Estimation, Prentice Hall, Upper Saddle River, NJ.

ISBN-10: 0133457117
ISBN-13: 978-0133457117

Steven M. Kay, Fundamentals of Statistical Signal Processing Vol. 2 Detection, Prentice Hall, Upper Saddle River, NJ.

ISBN-10: 013504135X
ISBN-13: 978-0135041352

Textbook information for this course is available online through the appropriate bookstore website: For online courses, search the MBS website at http://ep.jhu.edu/bookstore.

Highly Recommended

Additionally, any of the following texts or other texts that you may have from previous courses may be useful for this course if you find yourself struggling with specific skills:

R. Duda, P. Hart, and D. Stork, Pattern Classification, John Wiley & Sons, New York, 2001.

Other Recommended Texts

S. Russel and P. Norvig, Artificial Intelligence A Modern Approach, Prentice Hall, Upper Saddle River, NJ, 2003.

T. Hastie, R.Tibshirani, and J.Friedman, The Elements of Statistical Learning, Springer-Verlag, New York, 2001.

R. McDonough and A. Whalen, Detection of Signals in Noise, Academic Press, New York, 1995.

Required Software

MATLAB

You will need access to a recent version of MATLAB with the Signal Processing Toolkit. The MATLAB Total Academic Headcount (TAH) license is now in effect. This license is provided at no cost to you. Send an email to software@jhu.edu to request your license file/code. Please indicate that you need a standalone file/code. You will need to provide your first and last name, as well as your Hopkins email address. You will receive an email from Mathworks with instructions to create a Mathworks account. The MATLAB software will be available for download from the Mathworks site.

Student Coursework Requirements

It is expected that each module will take approximately 7–10 hours per week to complete. Here is an approximate breakdown: reading the assigned sections of the texts (approximately 3–4 hours per week) as well as some outside reading, listening to the audio annotated slide presentations (approximately 2–3 hours per week), and Problem Exercises/Lab Exercises (approximately2–3 hours per week).

This course will consist of four basic student requirements:

Preparation and Participation (Module Discussions) (10% of Final Grade Calculation)

You are responsible for carefully reading all assigned material and being prepared for discussion. The majority of readings are from the course text. Additional reading may be assigned to supplement text readings.

Responses to the discussion questions are required for each module week. Posting a response to the discussion question is part one of your grade for module discussions (i.e., Timeliness).

Part two of your grade for module discussion is your interaction (i.e., responding to classmate postings with thoughtful responses) with classmates (i.e., Critical Thinking). Be detailed in your postings and in your responses to your classmates'postings. Feel free to agree or disagree.

Please ensure that your postings are civil and constructive.

The instructor will monitor module discussions and will respond to some of the discussions as postings are submitted. In some instances, a summary of the overall discussions will be posted.

Evaluation of preparation and participation is based on contribution to discussions. A minimum of 2 discussion postings are required per week.

An optional alternative to 2 discussion postings is to attend weekly online office hours. Office hours attendance will count as 1 posting.

Problem Exercises (20% of Final Grade Calculation)

Problem Exercises are composed of quantitative problem sets. These should be worked thoroughly on paper, scanned to computer, and uploaded to the appropriate location in Black Board. Submissions should be in PDF format.

All Problem Exercises are due according to the dates in the Course Outline.

Late submissions will be reduced by ten points for each week late. A one week extension may be granted by the instructor, but must be coordinated ahead of time.

Problem Exercises are evaluated by the following grading elements:

1. Each part of the problem is answered (20%).
2. Assumptions are clearly stated (20%).
3. Intermediate derivations and calculations are provided (25%).
4. Answer is technically correct and is clearly indicated (25%).
5. Answer precision and units are appropriate (10%).

Students are encouraged to interact in preparation of Problem Exercises. However, all submitted work is to be that of the individual student.

Lab Exercises (20% of Final Grade Calculation)

MATLAB is a powerful tool for illustrating course concepts and building programming skills. Lab Exercises will be assigned regularly. Students are not required to implement solutions in MATLAB. Other programming languages may be used. However, the outputs required by the lab will have to be submitted.

Students are encouraged to interact in preparation of Lab Exercises. However, all submitted work is to be that of the individual student.

Exams (50% of Final Grade Calculation, combined from 25% for Midterm and 25% for Final)

The midterm exam will be available in Module 7 and the final exam will be available in Module 14. You will have one week to complete the exams and they will be due by 11:59PM exactly one week from their release. Exams are open book and open notes.

New exam problems are developed every semester. Past exams may be provided by the instructor for student benefit.Students are discouraged from interaction within the exam period. Posting exams and solutions to E-Bay or similar sites during the exam period will violate the honor code.

Extensions are generally not allowed for exams. Students with conflicts should contact the instructor. In most cases regarding the exam, the chair of the ECE program will need to be consulted for approval.

Grading Policy

Assignments are due according to the dates posted in your Canvas course site. You may check these due dates in the Course Calendar or the Assignments in the corresponding modules. The instructor will post grades one week after assignment due dates.

Spelling and grammar is generally not graded. However, egregious violations of the rules of the English language will be noted without comment. Consistently poor performance in either spelling or grammar is taken as an indication of poor written communication ability that may detract from your grade.

A grade of A indicates achievement of consistent excellence and distinction throughout the course--that is, conspicuous excellence in all aspects of assignments and discussion in every week.

A grade of B indicates work that meets all course requirements on a level appropriate for graduate academic work. These criteria apply to both undergraduates and graduate students taking the course.

Final grades will be determined by the following weighting:

Academic Policies

Deadlines for Adding, Dropping and Withdrawing from Courses

Students may add a course up to one week after the start of the term for that particular course. Students may drop courses according to the drop deadlines outlined in the EP academic calendar (https://ep.jhu.edu/student-services/academic-calendar/). Between the 6th week of the class and prior to the final withdrawal deadline, a student may withdraw from a course with a W on their academic record. A record of the course will remain on the academic record with a W appearing in the grade column to indicate that the student registered and withdrew from the course.

Academic Misconduct Policy

All students are required to read, know, and comply with the Johns Hopkins University Krieger School of Arts and Sciences (KSAS) / Whiting School of Engineering (WSE) Procedures for Handling Allegations of Misconduct by Full-Time and Part-Time Graduate Students.

This policy prohibits academic misconduct, including but not limited to the following: cheating or facilitating cheating; plagiarism; reuse of assignments; unauthorized collaboration; alteration of graded assignments; and unfair competition. Course materials (old assignments, texts, or examinations, etc.) should not be shared unless authorized by the course instructor. Any questions related to this policy should be directed to EP’s academic integrity officer at ep-academic-integrity@jhu.edu.

Students with Disabilities - Accommodations and Accessibility

Johns Hopkins University values diversity and inclusion. We are committed to providing welcoming, equitable, and accessible educational experiences for all students. Students with disabilities (including those with psychological conditions, medical conditions and temporary disabilities) can request accommodations for this course by providing an Accommodation Letter issued by Student Disability Services (SDS). Please request accommodations for this course as early as possible to provide time for effective communication and arrangements.

For further information or to start the process of requesting accommodations, please contact Student Disability Services at Engineering for Professionals, ep-disability-svcs@jhu.edu.

Student Conduct Code

The fundamental purpose of the JHU regulation of student conduct is to promote and to protect the health, safety, welfare, property, and rights of all members of the University community as well as to promote the orderly operation of the University and to safeguard its property and facilities. As members of the University community, students accept certain responsibilities which support the educational mission and create an environment in which all students are afforded the same opportunity to succeed academically. 

For a full description of the code please visit the following website: https://studentaffairs.jhu.edu/policies-guidelines/student-code/

Classroom Climate

JHU is committed to creating a classroom environment that values the diversity of experiences and perspectives that all students bring. Everyone has the right to be treated with dignity and respect. Fostering an inclusive climate is important. Research and experience show that students who interact with peers who are different from themselves learn new things and experience tangible educational outcomes. At no time in this learning process should someone be singled out or treated unequally on the basis of any seen or unseen part of their identity. 
 
If you have concerns in this course about harassment, discrimination, or any unequal treatment, or if you seek accommodations or resources, please reach out to the course instructor directly. Reporting will never impact your course grade. You may also share concerns with your program chair, the Assistant Dean for Diversity and Inclusion, or the Office of Institutional Equity. In handling reports, people will protect your privacy as much as possible, but faculty and staff are required to officially report information for some cases (e.g. sexual harassment).

Course Auditing

When a student enrolls in an EP course with “audit” status, the student must reach an understanding with the instructor as to what is required to earn the “audit.” If the student does not meet those expectations, the instructor must notify the EP Registration Team [EP-Registration@exchange.johnshopkins.edu] in order for the student to be retroactively dropped or withdrawn from the course (depending on when the "audit" was requested and in accordance with EP registration deadlines). All lecture content will remain accessible to auditing students, but access to all other course material is left to the discretion of the instructor.