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ORCID

https://orcid.org/0000-0002-6423-8566

Access Type

Open Access Thesis

Document Type

thesis

Degree Program

Electrical & Computer Engineering

Degree Type

Master of Science in Electrical and Computer Engineering (M.S.E.C.E.)

Year Degree Awarded

2022

Month Degree Awarded

May

Abstract

Synthetic Aperture Radar (SAR) is a well-known radar technique widely used for 2D and 3D imaging since the 1960s. SAR systems offer all-weather, day and night, high-resolution imaging by using the motion of the antenna over the target of interest. By later use of signal processing techniques, the received echoes can be put back together using precise knowledge of the system location and geometry to obtain the distinct high-resolution SAR imagery.

Moreover, when the system is equipped with multiple antennas, topography and topographic changes in the region of interest can be inferred using interferometric techniques.

The Microwave Remote Sensing Laboratory (MIRSL) has developed multiple Synthetic Aperture Radar Systems over the past years. These systems have been tested in different data collection campaigns. For instance, the Ku-band system was used in the Trail Valley Creek region in Canada during the 2018-19 snow season. In addition, data processing capabilities were also developed in the past years allowing MIRSL to obtain their own Synthetic Aperture Radar products without any third-party organization interaction.

The scope of this thesis is to develop a tool to provide data validation and troubleshooting resources to the data acquired and processed using the radar systems and processing tools from MIRSL. This can be achieved by simulating ideal radar data and later running it through the processing software developed by the laboratory.

The simulator can generate artificial raw data from a wide range of input variables that may have an impact on the acquisition of real radar data. The platform attitude, miss-calibrations of the system, or even processing errors may be sources of important errors when obtaining SAR images. For this reason, this simulator can be used to generate raw data products in a controlled environment for further data validation and plausible error troubleshooting.

DOI

https://doi.org/10.7275/28591552

First Advisor

Professor Paul Siqueira

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