Projecte llegit
Títol: RFI mitigation of Synthetic Aperture Interferometric Radiometer (SAIR) using Empirical Mode Decomposition (EMD)
Estudiants que han llegit aquest projecte:
DA PENA RIBALTA, MARIA (data lectura: 27-10-2025)- Cerca aquest projecte a Bibliotècnica
DA PENA RIBALTA, MARIA (data lectura: 27-10-2025)Director/a: PARK, HYUK
Departament: FIS
Títol: RFI mitigation of Synthetic Aperture Interferometric Radiometer (SAIR) using Empirical Mode Decomposition (EMD)
Data inici oferta: 03-02-2025 Data finalització oferta: 03-10-2025
Estudis d'assignació del projecte:
- GR ENG SIST AEROESP
| Tipus: Individual | |
| Lloc de realització: EETAC | |
| Paraules clau: | |
| RFI, Synthetic Aperture Radiometer, SMOS | |
| Descripció del contingut i pla d'activitats: | |
| - **OBJECTIVES:**
This study aims to develop a novel approach for mitigating Radio Frequency Interference (RFI) in SAIR images using Two-Dimensional Empirical Mode Decomposition (2D-EMD). The specific objectives include: 1. Evaluating the suitability of 2D-EMD for RFI mitigation in remote sensing signals. 2. Developing adaptive 2D-EMD algorithms to adjust dynamically to specific RFI patterns. 3. Assessing the performance of 2D-EMD compared to existing methods, focusing on signal-to-noise ratio improvement and data quality enhancement. 4. Validating the results through simulated scenarios and real-world datasets (e.g., SMOS data). The findings will contribute to improving the quality of remote sensing data affected by RFI. - **METHODOLOGY:** To achieve the research objectives, the methodology follows these steps: - Study and implement EMD and 2D-EMD. - Acquire SMOS images contaminated with RFI or simulated images. - Apply 2D-EMD to the images and analyze the results. - Assess results and improve the algorithm. - Compare 2D-EMD with other SMOS RFI mitigation methods. The research will leverage previous work, including the **SAIR/SMOS simulator (SEPS/SAIRPS)**, RFI-contaminated SMOS images from the **Barcelona Expert Center (BEC)**, and **subspace RFI mitigation algorithms** for comparison. - **EXPECTED RESULTS:** This research aims to enhance **SMOS data quality** through **2D-EMD RFI mitigation**, leading to: 1. **Improved Soil Moisture and Ocean Salinity Retrievals** - Reducing RFI artifacts will enhance measurement accuracy, providing more reliable insights into Earth's surface and ocean conditions. 2. **Increased Data Consistency** - The method ensures uniform data quality across different regions and environmental conditions, supporting long-term monitoring and trend analysis. 3. **Adaptability to Changing RFI Environments** - The adaptive algorithm can respond to evolving RFI sources, ensuring sustained SMOS data reliability in dynamic technological landscapes. |
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| Overview (resum en anglès): | |
| This project is based on the study and application of Empirical Mode Decomposition (EMD) and its two-dimensional extension, Bidimensional Empirical Mode Decomposition (BEMD), to minimize Radio Frequency Interference (RFI) in satellite observations. Satellite remote sensing missions, such as the SMOS (Soil Moisture and Ocean Salinity) satellite managed by the European Space Agency (ESA), monitor soil moisture and ocean surface salinity. Even so, the SMOS radiometers (MIRAS), which operate in the L-band, are vulnerable to RFI. This interference appears in the brightness temperature (BT) images as intense peaks, compromising data quality.
Traditional RFI mitigation methods often struggle with the non-linear and non-stationary behavior of interference. BEMD is introduced as an adaptive, empirical alternative that decomposes an image into Intrinsic Mode Functions (IMFs), enabling the extraction and removal of high-frequency components associated with RFI while preserving low-frequency information and overall trends. Initial work validated the effectiveness of the EMD methodology by applying it to noisy Global Navigation Satellite System (GNSS) signals. Quantitative results demonstrated that EMD successfully restored critical signal information: for instance, in "File_02," the number of visible satellites increased from 3 to 4, allowing for position determination. Similarly, "File_03," which initially yielded information from only one satellite, was enhanced to provide data from four satellites after EMD application. For the core objective, a personalized BEMD algorithm was developed in MATLAB to process SMOS BT images. The decomposition successfully isolated the RFI, which manifests as high-frequency components (typically in the initial IMFs), demonstrating efficacy against both single and multiple interference sources within the same BT image. Furthermore, to optimize results given the data's geometry, the project investigated modifying the BEMD interpolation process from a standard square mesh to a hexagonal mesh. This adaptation proved highly successful, enabling the interference to largely disappear, with remaining interference areas suppressed below the critical 350K threshold, a value significantly higher than the natural maximum BT of Earth's surface (typically below 320K) and indicative of RFI contamination. The study concludes that EMD and BEMD shows an adaptable strategy for interference mitigation in dynamic remote sensing environments. Implementing this adaptive technique in SMOS data processing can improve the accuracy of salinity and humidity maps, thereby benefiting climate modeling, water resource management, and oceanography |
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