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.

Voice quality is strongly linked to quality of life; those who suffer from voice disorders are adversely affected in their social, family, and professional relationships. An effort has been made to more fully understand the physics behind how the voice is created, specifically the fluid structure interactions that occur during vocal fold vibration. Many techniques have been developed and implemented to study both the motion of the vocal folds and the airflow that creates the motion. Until recently these techniques have sought to understand a highly three-dimensional phenomenon with 1D or 2D perspectives.This research focuses on the development and implementation of an experimental technique to obtain three-dimensional characterizations of vocal fold motion and fluid flow. Experiments were performed on excised human vocal fold models at the University Hospital Erlangen Medical School in Erlangen, Germany. A novel technique for tracking the motion of the vocal folds using multiple camera viewpoints and limited user interaction was developed. Four high-speed cameras (2000 fps) recorded an excised vocal fold model vibrating at 250 Hz. Based on the images from these four cameras a fully 3D reconstruction of the superior surface of the vocal folds was achieved. The 3D reconstruction of 70 consecutive time steps was assembled to characterize the motion of the vocal folds over eight cycles. The 3D reconstruction accurately modeled the observed behavior of vocal fold vibration with a clearly visible mucosal wave. The average reprojection error for this technique was on par with other contemporary techniques (~20 micrometers). A whole field, time resolved, three-dimensional reconstruction of the vocal fold fluid flow was obtained using synthetic aperture particle image velocimetry. Simultaneous 3D flow fields, subglottal pressure waves, and superior surface motion were presented for 2 consecutive cycles of oscillation. The vocal fold fluid flow and motion measurements correlated with behavior observed in previous three-dimensional studies. A higher resolution view of one full cycle of oscillation was compiled from 16 time resolved data sets via pressure data. The result was a full three-dimensional characterization of the evolution and disintegration of the glottal jet.

Synthetic Aperture Radar (SAR) is a type of radar system that can generate high-resolution images with which one can detect subtle changes on the scale of centimetres from space. It can operate in any weather condition and during both day and night, making it unique compared to optical sensors. SAR is used for applications such as environmental monitoring, surveillance, and earth observation. Its ability to penetrate clouds and, to some extent, vegetation, allows for insights into terrain, vegetation structure, and even subsurface features. The importance of modelling the generated data of a SAR system before initiating the construction and development of it cannot be overstated. This thesis presents the implementation of the Reverse BackProjection Algorithm (RBPA) designed to generate raw SAR data efficiently and accurately. The RBPA stands out with its flexibility, enabling researchers and designers to simulate and gauge the SAR system's effectiveness under diverse scenarios. This provides an easy way of fine-tuning configurations for distinct needs concerning scene geometries, orbits, and radar designs. Two versions of the RBPA were implemented, differing slightly in the theoretical approach of azimuth defocusing. On top of this, a bistatic mode and Terrain Observation by Progressive Scans (TOPS) acquisition mode was also implemented. The inclusion of these two modes were specifically due to their relevance for the upcoming European Space Agency (ESA) SAR mission, Harmony. The addition of the TOPS mode required a comprehensive design of the antenna framework. Moreover, this implementation also paves the way for simpler integration of modes in the future. The two versions of the RBPA were profiled, revealing the optimal system and parameter configurations.

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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.

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