RASPNet: A Benchmark Dataset for Radar Adaptive Signal Processing Applications

• URL: http://arxiv.org/abs/2406.09638v1
• Date: Fri, 14 Jun 2024 00:07:52 GMT
• Title: RASPNet: A Benchmark Dataset for Radar Adaptive Signal Processing Applications
• Authors: Shyam Venkatasubramanian, Bosung Kang, Ali Pezeshki, Muralidhar Rangaswamy, Vahid Tarokh,
• Abstract summary: The dataset, called RASPNet, consists of 100 realistic scenarios compiled over a variety of topographies and land types from across the contiguous United States. 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.
• Score: 20.589332431911842
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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