Skip to main content

Unmanned Air System-Synthetic Aperture Radar 2020

About This Course

In 2020, the BWSI Unmanned Air System – Synthetic Aperture Radar (UAS-SAR) program will offer students the opportunity to explore the field of radar imaging by building and flying a radar on a small UAS and using it to image objects around campus. The program consists of two components: (1) this online course spanning February through April 2019, open to all interested and committed students, and (2) a four-week summer program at MIT campus for a small group students from July 6 to August 2, 2020.

This online course will help students build a solid foundation in the fundamentals of UAS flight, linear algebra and coordinate transformations, the fundamentals of radar including the Doppler effect, and an introduction to the software tools used in radar processing. During the summer, students will work in small groups alongside mentors to gain hands-on experience building, integrating, and processing data from a radar to image objects around campus.


In order to get the most value from this course, participants are expected to have a working knowledge of the topics listed below.

  • High school level algebra
  • High school level geometry
  • High school level trigonometry
  • Programming fundamentals, i.e., the basic steps to take an algorithm to code

Course Staff

Course Staff Image #1

Dr. Ramamurthy Bhagavatula

Ramamurthy (Ramu) Bhagavatula is a member of the technical staff in the Airborne Radar Systems and Techniques Group at MIT Lincoln Laboratory. First joining in 2011, his work has focused on developing novel algorithms for exploitation of radar signals and data. He briefly left Lincoln Laboratory from 2016 to 2017 to work on Uber's autonomous vehicle program where he helped developed new radar sensors for autonomous navigation. Rejoining the Laboratory in 2017, he has resumed his work novel radar exploitation algorithm development. He has also heavily contributed to a number of multi-modal, multi-domain systems analysis efforts in collaboration with other groups at the Laboratory. He holds bachelors, masters, and doctoral degrees in electrical and computer engineering from Carnegie Mellon University where his work focused on signal/image processing and machine learning.