RASPNet: A Benchmark Dataset for Radar Adaptive Signal Processing Applications
This provides a standardized evaluation tool for the adaptive radar community, though it is incremental as it focuses on dataset creation rather than new methods.
The authors tackled the lack of a large-scale, realistic dataset for radar adaptive signal processing by creating RASPNet, a 16 TB dataset with 100 scenarios and 10,000 clutter realizations per scenario, which can benchmark radar and complex-valued learning algorithms.
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.