Title:
Scattering and Optimization Methods for Radar and Millimeter Wave Applications
Scattering and Optimization Methods for Radar and Millimeter Wave Applications
Author(s)
Huang, Eric
Advisor(s)
Swaminathan, Madhavan
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Abstract
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.
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Date Issued
2022-12-06
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Resource Type
Text
Resource Subtype
Dissertation