New methods for radio frequency interference mitigation in microwave radiometry
(English) Radio Frequency Interference (RFI) has emerged as the most critical challenge to the effective exploitation of the microwave spectrum for Earth observation. RFI consists of non-natural emissions that obscure the radiometric signals measured by microwave radiometers. The presence of RFI deg...
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| Tipo de recurso: | tesis doctoral |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2117/439056 |
| Acceso en línea: | https://hdl.handle.net/2117/439056 https://dx.doi.org/10.5821/dissertation-2117-439056 |
| Access Level: | acceso abierto |
| Palabra clave: | Radiometry RFI Interference Microwave Remote Sensing 621.3 - Enginyeria elèctrica. Electrotècnia. Telecomunicacions Àrees temàtiques de la UPC::Enginyeria de la telecomunicació |
| Sumario: | (English) Radio Frequency Interference (RFI) has emerged as the most critical challenge to the effective exploitation of the microwave spectrum for Earth observation. RFI consists of non-natural emissions that obscure the radiometric signals measured by microwave radiometers. The presence of RFI degrades the accuracy and completeness of radiometric measurements. This is evident, for example, in the European Space Agency’s (ESA) Soil Moisture and Ocean Salinity (SMOS) mission, which, despite operating in the protected L-band, is severely affected by interference. This PhD thesis presents innovative approaches to mitigate this issue, with a particular focus on Synthetic Aperture Interferometric Radiometers, which have been insufficiently studied in the context of RFI. This PhD thesis introduces several novel RFI detection and mitigation techniques, including autocorrelation-based detection, non-linear decomposition using Empirical Mode Decomposition (EMD), and data-adaptive mitigation via the Karhunen-Loève Transform (KLT). These methods address some of the limitations of existing approaches and have been evaluated with simulated data using standard performance metrics. Furthermore, a new algebraic interpretation of mitigation is provided, bridging the gap between RFI detection and signal transformation methods, and introducing novel performance evaluation metrics. In addition to the theoretical contributions, this thesis includes experimental validation of the proposed techniques using real-world data. The practical feasibility and performance of whiteness-based detection and KLT-based mitigation techniques are demonstrated in realistic scenarios. The research also explores the effects of signal quantization on mitigation techniques, a relevant concern for digital radiometer implementations. |
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