Scattering and Optimization Methods for Radar and Millimeter Wave Applications

Analysis of EM waves from both the scattering model and hardware sides has grown exponentially since the requirements of electrical devices have become stricter in order to serve various applications. High performance computing based EM emulators are used to simulate real-time complex EM wave interactions between multiple radar targets, transmitters, and receivers. The RCS of the radar targets are required to be stored as a table; however, the needed storage size increases dramatically with the angle and frequency sampling density. We present innovative approaches of constructing concise anisotropic point scatterer models that the emulators can use as part of the computations. Another arising application of EM analysis is the microwave design. Bayesian Optimization is a machine learning based method that enables fast convergence toward a global optimum. A general problem with this method is the low dimensionality of the problem that needs to be addressed which is contrary to microwave design that often requires many parameters to be optimized with precision. We propose and apply a high dimensional Bayesian Optimization to two emerging system designs, namely the beamforming antenna in package design for wireless communication and the wireless power transfer for IoT.

Level thesis:degree_level

Doctoral

Department dc:contributor.department

Electrical and Computer Engineering

Grantor dc:publisher

Georgia Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Huang, Eric

Advisor dc:contributor.advisor

• Swaminathan, Madhavan

Committee members dc:contributor.committeemember

• Romberg, Justin
• Mukhopadhyay, Saibal
• Peterson, Andrew F.
• Sitaraman, Suresh K.

• Radar
• RCS
• High performance computing
• Point scatterer mode
• Microwave design
• Optimization

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/1853/70154

OAI identifier oai:identifier

oai:repository.gatech.edu:1853/70154