525.748.81 - Synthetic Aperture Radar

Expanded Course Description

This course covers the basics of synthetic aperture radar (SAR). In particular, the course will examine why there are limiting design considerations for real aperture radar, and how a synthetic aperture can overcome these limitations to create high-resolution radar imaging. Stripmap and spotlight SAR will be compared and contrasted. Spotlight SAR technology will be compared to computerized axial tomography (CAT). Signal processing of the SAR data will be covered, including motion compensation, Doppler beam-sharpening, polar formatting, aperture weighting (or apodization), and autofocus. Advanced topics will include interferometric processing of SAR data, a brief overview of bi-static SAR, moving targets in SAR, and the difficulty in estimating motion of targets in single-channel SAR. Students will work through problems involving radar and synthetic aperture radar processing. Over the life of the course, each student will develop a SAR simulator that will generate synthetic data based on simple point scatterers in a benign background. The simulator will include an image formation processor, based on modules built by the student.

Course Structure

The course materials are divided into modules which can be accessed by clicking Course Modules on the left menu. A module will have several sections including the overview, content, readings, discussions, and assignments. You are encouraged to preview all sections of the module before starting. Most modules run for a period of seven (7) days, exceptions are noted in the Course Outline. You should regularly check the Calendar and Announcements for assignment due dates.

Course Topics

Introduction to Synthetic Aperture Radar

Radar Concepts and the Linear FM Chirp

Synthetic Aperture Concepts

SAR Signal Characteristics

The Range Doppler Algorithm (Low Squint Case)

The Range Doppler Algorithm (Significant Squint Case)

Other Image Formation Algorithms for Demodulated SAR Data

Dechirp-On-Receive

Polar Formatting

Backprojection

Multilook, Aperture Weighting, and Autofocus

CWLFM Synthetic Aperture Radar

Interferometry and Polarimetry

Moving Targets

Course Goals

To explain the concept of high resolution imaging radar using a synthetic aperture.  The student should be able to describe the algorithms used to “focus” the synthetic aperture radar (SAR) image and which algorithms are associated with either demodulation or dechirp.  The course also seeks to expose the student to various applications of SAR, including interferometry and moving target detection.

Course Learning Outcomes (CLOs)

• Explain the concept of high resolution imaging with radar using a synthetic aperture
• Describe the algorithms used to “focus” the synthetic aperture radar (SAR) image and which algorithms are associated with demodulation or dechirp
• Analyze simulated and real SAR data for quality issues such as focus and raised sidelobes; implement and execute the proper algorithmic techniques to improve image quality
• Identify the unique characteristics of SAR evident in imagery, such as layover, as well as the ability to visually interpret a SAR scene
• Explain various coherent SAR products, such as interferometric SAR and moving target detection/characterization
• Analyze various image formation algorithms, identifying their advantages and disadvantages, as well as which are appropriate for a particular data set
• Develop code in MATLAB or Python to simulate SAR data, implement image formation algorithms, analyze image quality, and improve that quality with various well-known techniques

Textbooks

REQUIRED:

• E. D. Jansing, Introduction to Synthetic Aperture Radar: Concepts and Practice, McGraw-Hill, ISBN 9781260458961, 2021.

OPTIONAL:

• I. G. Cumming and F. H. Wong,  Digital Processing of Synthetic Aperture Radar Data:  Algorithms and Implementation.  Boston, MA:  Artech House, 2005.

• C. V. J. Jakowatz, D. E. Wahl, P. H. Eichel, D. C. Ghiglia, and P. A. Thompson, Spotlight-Mode Synthetic Aperture Radar:  A Signal Processing Approach, Boston, MA:  Springer, 1996.

Required Software

Students will be required to complete computer assignments in either MATLAB or Python.  No other simulation or programming languages will be accepted for this course.

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.

Python

While any Python distribution is acceptable (Linux and Mac machines come with Python pre-installed), Anaconda Python is highly recommended (https://www.continuum.io/anaconda-overview).  Anaconda is free, installs in the user’s home directory so there is no interaction/mixing with the operating system’s Python, and contains all of the packages necessary to do scientific computing.

Student Coursework Requirements

It is expected that each module will take approximately 6–11 hours per week to complete. Here is an approximate breakdown: reading the assigned sections of the texts (approximately 1–2 hours per week) as well as some reading not in the text (such as journal articles), listening/watching video lectures (approximately 1 hour per week), homework assignments (approximately 3-5 hours per week), and collaboration assignments (approximately 1-3 hours per week).  Not all modules will have collaboration assignments.

This course will consist of the following basic student requirements:

Assignments (50% of the overall grade)
Quizzes (30% of the overall grade)
Collaboration (20% of the overall grade)

• Assignments:  Students should approach each module in the order it's presented in Canvas.  This includes reading (both in the textbook and external papers, which will be available in eReserves), watching short lecture videos, and completing assignments.  The assignments will contain quantitative questions, qualitative questions (such as essay questions), and computer simulation work.  The point value for each problem in the assignment will be posted with the question.  Students are encouraged to work together, but not copy, on assignments.
  
  
• Quizzes:  There are two quizzes, a mid-term quiz during Module 7 and a final quiz during Module 14.  Students shall not collaborate on the quizzes.  Quizzes will be given via Canvas and consist of short answer, multiple choice, calculations, and short essays.  In some cases, the student will be given a grade automatically after the quiz is submitted.  The grades are not final until the instructor has reviewed each and every quiz.  In some cases, Canvas can be literal about given answers (especially for short answer questions), marking the answer incorrect when, in fact, it might be correct.  The instructor will review each quiz and correct any mistakes made in the automatic grading process.  Students should not panic about the grade until the instructor has reviewed all of the quizzes.  If the student still has concerns, then they may bring the issue to the instructor's attention.
  
  
• Collaboration:  Students will be required to work together in teams.  Each team will be responsible for posting collaborative materials via Microsoft Teams.  A team may be asked to participate in an informal debate on a SAR related topic with the instructor or another team.  Teams will conduct case studies on SAR related topics.  Each team will be required to present a capstone project (virtually) based on a specific SAR application or problem.  Each directed assignment will have the points its worth posted in the written directions.  Students are encouraged to do the assignments and quizzes first, then work on the collaborative pieces.  This will ensure that each student has the proper technical foundation in which to participate fully.  Collaboration must be done interactively and collaboratively, as the name suggests.

Late Policy:  Students should make every effort to turn Assignments, Discussions/Collaborations, and Quizzes on time.  Late submissions for Assignments and Discussions/Collaborations will result in a 5% penalty each day the submission is late, up to one week.  After that week, the submission will no longer be considered.  For example, if an assignment is worth 100 points, then each day the submission is late, 5 points will be deduced from the final grade, up to 35 points (7 days x 5 points = 35 points/week).  After one week, the Assignment or Discussion/Collaboration will become a zero.  For example, if the assignment is due on Tuesday at 23:59 Eastern, then the absolute latest you can turn in the assignment would be on Monday at 23:59 Eastern.  Students may coordinate with the instructor for a waiver of the late penalty and submission timeline; this must be done before the due date.  Acceptable excuses include, but are not limited to, travel for work, family issues/emergencies, planned vacations.  If you work with the instructor well in advance, there should be no issues.  The Quizzes will be unavailable after each module, so there is no opportunity for a late submission.  So, make every effort to finish the quiz during that module week.

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. I/We will post grades one week after assignment due dates.

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.

EP uses a +/- grading system (see “Grading System”, Graduate Programs catalog, p. 10). You should contact your Program Chair for guidance on the breakdown used by your program.

Overall Percentage Points for Course:

Letter Grade	Score Range
A+	100-98
A	97-94
A-	93-90
B+	89-87
B	86-83
B-	82-80
C+	79-77
C	76-73
C-	72-70
D+	69-67
D	66-63
F	63-0

Final grades will be determined by the following weighting:

Course Expectation	% of Overall Grade
Assignments	50
Quizzes	30
Collaboration	20

 

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.