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| | # 🚀 RFInject: Synthetic RF Interference Injection for Sentinel-1 SAR L0 Data |
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| | ## 📌 Motivation |
| | - **Radio Frequency Interference (\gls{RFI})** is a **major source of performance degradation** in modern **Synthetic Aperture Radar (\gls{SAR})** missions. |
| | - The **Copernicus Sentinel-1 constellation** is significantly affected, with numerous studies reporting its **detrimental impact**. |
| | - However, the **lack of standardized and reproducible datasets** has so far **limited systematic benchmarking** of RFI detection and mitigation strategies. |
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| | ## 🛠️ What RFInject Brings |
| | **RFInject** introduces a **methodology for controlled synthetic RFI injection** into clean Sentinel-1 L0 raw bursts, enabling: |
| | - ✅ **Reproducible benchmarking** of mitigation algorithms |
| | - ✅ **Realistic simulation** while retaining authentic system properties |
| | - ✅ **Full parameter control** over RFI characteristics |
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| | ## 📐 Methodology Highlights |
| | The framework is based on a **parametric signal model**: |
| | - 🎯 **Synthetic RFI generation** by superimposing **modulated chirp trains** onto authentic Sentinel-1 radar echoes. |
| | - 🧠 **Spectral and statistical fidelity** ensured to reflect real operational systems. |
| | - 📊 **Metadata-rich parameter sets** controlling: |
| | - 📡 Waveform diversity |
| | - 🌍 Spatial extent |
| | - ⚡ Power scaling |
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| | ## 📂 Dataset Features |
| | - **Clean Sentinel-1 L0 bursts** → contaminated with **controlled synthetic RFI** |
| | - **Fully reproducible** contamination scenarios |
| | - **Rich metadata** for systematic testing across **different algorithms** and **experimental setups** |
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| | ## 🎯 Impact and Applications |
| | The dataset empowers researchers to: |
| | - 🕵️♂️ **Detect** RFI more reliably |
| | - 🛡️ **Mitigate** its impact effectively |
| | - 🤖 Develop **learning-based solutions** for robust **RFI-resilient SAR processing pipelines** |
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