RASPNet: A Benchmark Dataset for Radar Adaptive Signal Processing Applications
<|reference_start|>RASPNet: A Benchmark Dataset for Radar Adaptive Signal Processing Applications: This work presents a large-scale dataset for radar adaptive signal processing (RASP) applications, aimed at supporting the development of data-driven models within the radar community. The dataset, called RASPNet, consists of 100 realistic scenarios compiled over a variety of topographies and land types from across the contiguous United States, designed to reflect a diverse array of real-world environments. Within each scenario, RASPNet consists of 10,000 clutter realizations from an airborne radar setting, which can be utilized for radar algorithm development and evaluation. RASPNet intends to fill a prominent gap in the availability of a large-scale, realistic dataset that standardizes the evaluation of adaptive radar processing techniques. We describe its construction, organization, and several potential applications, which includes a transfer learning example to demonstrate how RASPNet can be leveraged for realistic adaptive radar processing scenarios.<|reference_end|>
@article{venkatasubramanian2024raspnet:,
    title={RASPNet: A Benchmark Dataset for Radar Adaptive Signal Processing
  Applications},
    author={Shyam Venkatasubramanian, Bosung Kang, Ali Pezeshki, Muralidhar
  Rangaswamy, Vahid Tarokh},
    journal={arXiv preprint arXiv:2406.09638},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.09638},
    primaryClass={cs.LG eess.SP}
}