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