*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************
Title: A Geometrical Variational Approach to Shape Inversion for Radar
Committee:
Dr. Anthony Yezzi, ECE, Chair , Advisor
Dr. Christopher Barnes, ECE
Dr. Aaron Lanterman, ECE
Dr. Patricio Vela, ECE
Dr. Thomas Orlando, Chemistry
Abstract:
In this thesis, we develop a novel method for dense shape reconstruction of scenes using radar. For a given scene and antennas taking measurements from the scene, our method iteratively estimates the scene shape using the measurements. To this end, we use a deformable shape evolution approach which seeks to match the received signal to a computed forward model based on the evolving shape. Adopting such an approach comes with important advantages such as the ability to naturally embed the shape priors into the estimation and being able to model self-occlusions which cannot be easily incorporated into classical radar imaging techniques. Iterations start with an initial shape which is gradually deformed until its image under the forward model gets sufficiently close to the actual measurements. Since we use a gradient-based scheme to minimize our error and radar signals are highly oscillatory, a special attention is required to prevent these oscillations to manifest in the cost functional as local minima. For this purpose, we develop a novel technique by which we can extract the geometric information embedded in the radar signals to that is used to formulate the cost functional. We test our approach with synthetic simulations performed in 2D which shows the promise of our approach on some challenging scenarios.
