Summary of Raspnet: a Benchmark Dataset For Radar Adaptive Signal Processing Applications, by Shyam Venkatasubramanian et al.
RASPNet: A Benchmark Dataset for Radar Adaptive Signal Processing Applications
by Shyam Venkatasubramanian, Bosung Kang, Ali Pezeshki, Muralidhar Rangaswamy, Vahid Tarokh
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a large-scale dataset called RASPNet, designed for radar adaptive signal processing (RASP) applications. The dataset contains 100 realistic scenarios of airborne radar settings from various topographies and land types across the contiguous United States. Each scenario has 10,000 clutter realizations, suitable for benchmarking radar algorithms and complex-valued neural networks. The authors aim to fill a gap in the availability of large-scale, realistic datasets for evaluating adaptive radar processing techniques. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The dataset is over 16 TB in size and can be used to develop data-driven models within the adaptive radar community. It has several applications, including transfer learning, which demonstrates how RASPNet can be used for realistic adaptive radar processing scenarios. |
Keywords
» Artificial intelligence » Signal processing » Transfer learning
