Dissertation Defense Announcement for Dylan Boyd – 06/19/2023 at 10:00 AM

May 16, 2023

Dissertation Title:
Exploring bistatic scattering modeling for land surface applications using radio spectrum recycling in the Signal of Opportunity Coherent Bistatic Simulator

When: 06/19/2023 10:00 AM

Where: Simrall 228

Candidate: Dylan R. Boyd

Degree: Doctor of Philosophy in Electrical and Computer Engineering

Committee Members:
Dr. Mehmet Kurum,
Dr. John E. Ball
Dr. J. Patrick Donoho
Dr. Ali C. Gurbuz

In recent years, engineers have successfully recycled navigation and communication signals such as the global positioning system (GPS) for the purpose of environmental remote sensing. In this context, this semi-active radar technique is referred to as Signals of Opportunity (SoOp) remote sensing. Through this recycling, scientists can make use of globally available anthropogenic signals which would otherwise be unavailable. Of particular interest are signals with longer wavelengths than L-band GPS, which can provide new data in difficult-to-sense areas such as dense vegetation, snow-covered terrain, and subsurface soils. Accurate electromagnetic modeling of these new systems is essential for correctly interpreting this new data.

These new signals come with increased computational demands for models and simulators. The use of bistatic radar in place of monostatic radar increases the number of potential scattering geometries over earth terrain. Using signals not intended for remote sensing can significantly increase the surface scattering into the receiver. The need for observations in new, complex environments also requires that simulators adequately model these terrains. Plainly speaking, this new data generates demand for increased computations across both vertical and horizontal spatial dimensions in addition to simulating electrically complex mediums.

This dissertation addresses these increased computational demands by extending the open-source model known as the SoOp Coherent Bistatic Scattering model and simulator (SCoBi). The SCoBi model is extended to simulate subsurface soil reflections and large-scale topography. For subsurface remote sensing, physical and statistical simulation studies are performed to provide recommendations for maximizing sensitivity to changes in subsurface soils. For large-scale topography simulations, we present a solution to the Stratton-Chu integral, which makes use of a family of nested rectangular facets which can estimate surface and subsurface scattering. These solutions provide computationally efficient methods which can further aid in designing SoOp receivers and interpreting their measurements.