This thesis presents a design methodology for continuous wave (CW) synthetic aperture radar (SAR) systems. The focus is on design considerations specific to small, low-power systems suitable for operation on small aircraft and unmanned aerial vehicles (UAVs). Well-known results which have been derived in other works, such as the radar equation, are explained in the context of low-power, CW systems. Additionally, design issues unique to CW SAR are addressed and the results generalized. A method for controlling feedthrough between antennas is developed, and the resulting limitations on swath width are discussed. Methods are developed which allow an engineer to design a CW SAR system to obtain a given swath width, resolution, and data rate, and necessary tradeoffs are discussed. Using the proposed methodology, designs for two specific SAR systems are developed. Example sections outline the design of two small SAR systems called microASAR and microBSAR. These sections present a real-world application of the methodology and offer explanations of the rationale behind many of the design choices. Straightforward methods for testing different aspects of a completed SAR system are developed and presented. These procedures are carried out using microASAR hardware, and the results are used to validate the design methodology.

This thesis presents the development of a high-resolution ground-based 3D-SAR system and investigates its application to microwave-vegetation studies. The development process of the system is detailed including an enumeration of high-level requirements, discussions on key design issues, and detailed descriptions of the system down to a component level. The system operates on a 5.4 GHz (C-band) signal, provides a synthetic aperture area of 1.7 m x 1.7 m, and offers resolution of 0.75 m x 0.3 m x 0.3 m (range x azimuth x elevation). The system is employed on several trees with varying physical characteristics. The resulting imagery demonstrates successful 3D reconstruction of the trees and some of their internal features. The individual leaves and small branches are not visible due to the system resolution and the size of the wavelength. The foliage's outline and internal density distribution is resolved. Large branches are visible where geometry is favorable. Trunks are always visible due to their size and normal-facing incidence surface and their return has the strongest contribution from their base. The imagery is analyzed for dependencies on radar and tree parameters including: incidence angle, signal frequency, polarization, inclusion size, water content, and species. In the current work, a single frequency (5.4 GHz) and polarization (HH) is used which leaves the door open for future analysis to use other frequencies and polarizations. The improved resolution capabilities of the 3D-SAR system enables more precise backscatter measurements leading to a greater understanding of microwave-vegetation scattering behavior.

As implantable medical devices are being used more often to treat medical problems for which pharmaceuticals don’t suffice, it is important to understand their interactions with commonly used medical modalities. The interactions between medical implants and Magnetic Resonance Imaging machines have proven to be a risk for patients with implants. Implanted medical devices with elongated metallic components can create harmful levels of local heating in a Magnetic Resonance Imaging (MRI) environment [1]. The heating of a biological medium under MRI is monitored via the Specific Absorption Rate (SAR). SAR, defined as power absorbed per unit mass (W/kg), can be calculated as , where σ is electrical conductivity of the medium in units of , |E| is the magnitude of the applied electric field in units of , and ρ is the density of the medium in units of . For continuous, uniform power deposition this can be measured experimentally as a rise in temperature over time (∆T/t), where c is the specific heat capacity of the medium in units of. To understand the SAR induced in-vivo, a phantom (Figure 2.10) is used to conduct in-vitro experiments, as it provides a controllable and repeatable experimental setup. In order to experiment in the phantom, an understanding of the background SAR distribution and in turn the exposure field distribution of the phantom is required as per the ASTMF2182-09 standard [2]. In this work, the background SAR distribution of an ASTM standard torso phantom is measured and studied via fiber optic thermometry. The measurements are compared with an electromagnetic model simulated via FDTD, demonstrating agreement between 10-25%. A custom exposure and data collection setup (including oscilloscope, function generator, RF amplifier, directional coupler, and Neoptix Omniflex Fiber Optic Thermometry system) was integrated and automated using NI LabView. The purpose of this thesis is to map the field distribution in a torso phantom under RF exposure from a 64 MHz MRI RF Birdcage, compare the results to an electromagnetic simulation, and finally conclude the accuracy of this method for field measurements in a standard torso phantom. Understanding the capabilities and accuracy of the fiber optic thermometry method will ultimately allow researchers to successfully apply this method to monitor background fields in their respective experimental setups (related to MRI implant heating) and understand its limitations.

Synthetic Aperture Radar (SAR) has come into widespread use in several civilian and military applications. The focus of this paper is the military application of imaging point targets captured by an airborne SAR platform. Using the traditional SAR method of determining target motion by analyzing the difference between subsequent images takes a relatively large amount of processing resources. Using methods in this thesis, target motion can be estimated before even a single image is obtained, reducing the amount of time and power used by a significantly large amount. This thesis builds on work done by Brain Zaharri and David So. Brain Zaharri successfully created a SAR simulation that accurately models the airborne SAR system capturing data of a target space using the Range Doppler Algorithm (RDA). David So extended this work by adding functionality to determine target velocity in the range and azimuth directions by processing the intermittent data created by the several steps of Brian Zaharri’s simulation. This thesis shows further extensions of processing the intermittent data using unique methods. The methods in this thesis successfully demonstrate the ability to quickly and accurately estimate target position, velocity, and acceleration without the need for using multiple SAR images. Target motion in the range direction is detected without using any part of the RDA, while the azimuth direction cuts out several steps, including the range compression phase and the range cell migration correction. Removing these unneeded processing steps dramatically decreases target motion data acquisition time. Both Brian Zaharri’s and David So’s work, along with this thesis, are part of the Cal Poly SAR Automatic Target Recognition (ATR) group of projects, which is sponsored by Raytheon Space & Airborne Systems Division. Because U.S. military SAR data remains classified, the Cal Poly SAR ATR projects addresses the need to educate researchers on the processing of SAR data.

This thesis presents a deep learning tool able to identify ice in radar images fromthe sea-ice environment of the Twhaites glacier outlet. The project is motivatedby the threatening situation of the Thwaites glacier that has been increasingits mass loss rate during the last decade. This is of concern considering thelarge mass of ice held by the glacier, that in case of melting, could increasethe mean sea level by more than +65 cm [1]. The algorithm generated alongthis work is intended to help in the generation of navigation charts and identificationof icebergs in future stages of the project, outside of the scope of this thesis.The data used for this task are ICEYE’s X-band radar images from the Thwaitessea-ice environment, the target area to be studied. The corresponding groundtruth for each of the samples has been manually generated identifying the iceand icebergs present in each image. Additional data processing includes tiling,to increment the number of samples, and augmentation, done by horizontal andvertical flips of a random number of tiles.The proposed tool performs semantic segmentation on radar images classifyingthe class "Ice". It is developed by a deep learning Convolutional Neural Network(CNN) model, trained with prepared ICEYE’s radar images. The model reachesvalues of F1 metric higher than 89% in the images of the target area (Thwaitessea-ice environment) and is able to generalize to different regions of Antarctica,reaching values of F1 = 80 %. A potential alternative version of the algorithm isproposed and discussed. This alternative score F1 values higher than F1 > 95 %for images of the target environment and F1 = 87 % for the image of the differentregion. However, it must not be confirmed as the final algorithm due to the needfor further verification.

Natural disasters like flooding have always been a big problem for countries around the world, but as the global climate changes and the number of people living in cities keeps growing, the threat of flooding has become a lot worse. Even though many studies have been conducted on flood mapping and vulnerability assessment in urban areas, this research addresses a significant knowledge gap in this domain. First, we used a flood depth estimation approach has been used to address the overestimation of urban flood mapping areas using Sentinel-1 images. Ten different combinations of the two initial VH and VV polarizations were used to rapidly and accurately map urban floods within open-source Google Earth Engine platforms using four different methods. The inclusion of flood depth has improved the accuracy of these methods by 7% on average. Next, we focused our research to find out who is most at risk in the floodplain areas. Minority communities, such as African Americans, as a result of socioeconomic constraints, face more difficulties. So, next we conducted an analysis of spatial and temporal changes of demographic patterns (Race) in five southern cities in US. From our analysis we have found that in majority of cities, the minority population within the floodplain has increased over the past two decades, with the exception of Charleston, South Carolina, where the white population has increased while the minority population has decreased. Building upon these insights, we have included more socio-economic and demographic variables in our analysis to find out the more holistic view of the vulnerable people in two of these cities (Jackson and Birmingham). Due to high autocorrelation between explanatory variables, we used Principal Component Analysis (PCA) along with global and local regression techniques to find out how much these variables can explain the vulnerability. According to our findings, the spatial components play a significant role in explaining vulnerabilities in greater detail. The findings of this research can serve as an important resource for policymakers, urban planners, and emergency response agencies to make informed decisions in future events and enhancing overall resilience.

京都大学 / 新制・論文博士 / 博士(工学) / 乙第13544号 / 論工博第4207号 / 新制||工||1984(附属図書館) / (主査)教授 角 哲也, 教授 藤田 正治, 准教授 竹門 康弘 / 学位規則第4条第2項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Thermokarst is a typical process that indicates widespread permafrost degradation in yedoma landscapes. The Lena-Aldan interfluvial area in Central Yakutia in eastern Siberia is now facing extensive landscape changes with surface subsidence due to thermokarst development during the past few decades. To clarify the spatial extent and rate of subsidence, multiple spatial datasets, including GIS and remote sensing observations, were used to analyze the Churapcha rural locality, which has a typical yedoma landscape in Central Yakutia. Land cover classification maps for 1945 and 2009 provide basic information on anthropogenic disturbance to the natural landscape of boreal forest and dry grassland. Interferometric synthetic aperture radar (InSAR) with ALOS-2/PALSAR-2 data revealed activated surface subsidence of 2 cm/yr in the disturbed area, comprising mainly abandoned agricultural fields. Remote sensing with an unmanned aerial system also provided high-resolution information on polygonal relief formed by thermokarst development at a disused airfield where InSAR analysis exhibited extensive subsidence. It is worth noting that some historically deforested areas have likely recovered to the original landscape without further thermokarst development. Spatial information on historical land-use change is helpful because most areas with thermokarst development correspond to locations where land was used by humans in the past. Going forward, the integrated analysis of geospatial information will be essential for assessing permafrost degradation.

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