Catalyzed by the recent emergence of site-specific, high-fidelity radio
frequency (RF) modeling and simulation tools purposed for radar, data-driven
formulations of classical methods in radar have rapidly grown in popularity
over the past decade. Despite this surge, limited focus has been directed
toward the theoretical foundations of these classical methods. In this regard,
as part of our ongoing data-driven approach to radar space-time adaptive
processing (STAP), we analyze the asymptotic performance guarantees of select
subspace separation methods in the context of radar target localization, and
augment this analysis through a proposed deep learning framework for target
location estimation. In our approach, we generate comprehensive datasets by
randomly placing targets of variable strengths in predetermined constrained
areas using RFView, a site-specific RF modeling and simulation tool developed
by ISL Inc. For each radar return signal from these constrained areas, we
generate heatmap tensors in range, azimuth, and elevation of the normalized
adaptive matched filter (NAMF) test statistic, and of the output power of a
generalized sidelobe canceller (GSC). Using our deep learning framework, we
estimate target locations from these heatmap tensors to demonstrate the
feasibility of and significant improvements provided by our data-driven
approach in matched and mismatched settings.Comment: 39 pages, 24 figures. Submitted to IEEE Transactions on Aerospace and
Electronic Systems. This article supersedes arXiv:2201.1071