RASPNet: A Benchmark Dataset for Radar Adaptive Signal Processing Applications
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866911247664939008 |
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| author | Venkatasubramanian, Shyam Kang, Bosung Pezeshki, Ali Rangaswamy, Muralidhar Tarokh, Vahid |
| author_facet | Venkatasubramanian, Shyam Kang, Bosung Pezeshki, Ali Rangaswamy, Muralidhar Tarokh, Vahid |
| contents | We present a large-scale dataset called RASPNet for radar adaptive signal processing (RASP) applications to support the development of data-driven models within the adaptive radar community. RASPNet exceeds 16 TB in size and comprises 100 realistic scenarios compiled over a variety of topographies and land types across the contiguous United States. For each scenario, RASPNet comprises 10,000 clutter realizations from an airborne radar setting, which can be used to benchmark radar and complex-valued learning algorithms. RASPNet intends to fill a prominent gap in the availability of a large-scale, realistic dataset that standardizes the evaluation of RASP techniques and complex-valued neural networks. We outline its construction, organization, and several applications, including a transfer learning example to demonstrate how RASPNet can be used for real-world adaptive radar scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_09638 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RASPNet: A Benchmark Dataset for Radar Adaptive Signal Processing Applications Venkatasubramanian, Shyam Kang, Bosung Pezeshki, Ali Rangaswamy, Muralidhar Tarokh, Vahid Machine Learning Signal Processing We present a large-scale dataset called RASPNet for radar adaptive signal processing (RASP) applications to support the development of data-driven models within the adaptive radar community. RASPNet exceeds 16 TB in size and comprises 100 realistic scenarios compiled over a variety of topographies and land types across the contiguous United States. For each scenario, RASPNet comprises 10,000 clutter realizations from an airborne radar setting, which can be used to benchmark radar and complex-valued learning algorithms. RASPNet intends to fill a prominent gap in the availability of a large-scale, realistic dataset that standardizes the evaluation of RASP techniques and complex-valued neural networks. We outline its construction, organization, and several applications, including a transfer learning example to demonstrate how RASPNet can be used for real-world adaptive radar scenarios. |
| title | RASPNet: A Benchmark Dataset for Radar Adaptive Signal Processing Applications |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2406.09638 |