  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.
