Theses and Dissertations
Advisor
Gurbuz, Ali Cafer
Committee Member
Kurum, Mehmet
Committee Member
Marojevic, Vuk
Date of Degree
8-13-2024
Original embargo terms
Immediate Worldwide Access
Document Type
Graduate Thesis - Open Access
Major
Electrical & Computer Engineering
Degree Name
Master of Science (M.S.)
College
James Worth Bagley College of Engineering
Department
Department of Electrical and Computer Engineering
Abstract
Radio frequency interference (RFI) poses significant challenges for passive microwave radiometry used in climate studies and Earth science. Despite operating in protected frequency bands, microwave radiometers often encounter RFI from sources like air surveillance radars, 5G communications, and unmanned aerial vehicles. Traditional RFI detection methods rely on handcrafted algorithms designed for specific RFI types. This study proposes a deep learning (DL) approach, leveraging convolutional neural networks to detect various RFI types on a global scale. By learning directly from radiometer data, this data-driven method enhances detection accuracy and generalization. The DL framework processes raw moment data and Stokes parameters, dynamically labeled using quality flags, offering a robust and efficient solution for RFI detection. This approach demonstrates the potential for improved RFI mitigation in passive remote sensing applications.
Recommended Citation
Alam, Ahmed Manavi, "Deep learning-based radio frequency interference detection and mitigation for microwave radiometers with 2-D spectral features" (2024). Theses and Dissertations. 6215.
https://scholarsjunction.msstate.edu/td/6215