Date of Award

5-1-2024

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

College/School/Department

Department of Electrical and Computer Engineering

Dissertation/Thesis Chair

Mustafa Aksoy

Committee Members

Gary Saulnier, Hany Elgala, Junhong Wang

Keywords

deep learning, microwave radiometry, one class algorithms, Radio frequency interference, remote sensing, RFI detection

Subject Categories

Electrical and Electronics

Abstract

Measurements of natural electromagnetic radiation from Earth using microwave radiometers provide deep insight into our planet and its environmental conditions. These insights are essential for quantifying, understanding, and predicting various geophysical processes, such as climate patterns, water cycles, carbon cycles, and more. These passive measurements are diverse and cover a wide range of frequencies, depending on the sensitivity of microwave radiation to changes in important geophysical parameters. However, it is important to note that the microwave spectrum is also utilized by active services, such as wireless communication networks and radars. As a result, Radio Frequency Interference (RFI) in the measurements of Earth-observing radiometers has been reported. With the increasing demand for frequency spectrum by active services, the presence of RFI in these measurements is expected to increase. RFI contamination, if not properly detected and removed, may result in erroneous retrieval of critical geophysical parameters in passive remote sensing missions. To address this challenge, several detection and mitigation algorithms have been proposed and implemented. However, their success has been limited, particularly in cases of weak or noise-like interference.

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