Projecte matriculat
Títol: RFI mitigation of Synthetic Aperture Interferometric Radiometer (SAIR) using Empirical Mode Decomposition (EMD)
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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