  Leveraging the advanced functionalities of modern radio frequency (RF)
modeling and simulation tools, specifically designed for adaptive radar
processing applications, this paper presents a data-driven approach to improve
accuracy in radar target localization post adaptive radar detection. To this
end, we generate a large number of radar returns by randomly placing targets of
variable strengths in a predefined area, using RFView, a high-fidelity,
site-specific, RF modeling & simulation tool. We produce heatmap tensors from
the radar returns, in range, azimuth [and Doppler], of the normalized adaptive
matched filter (NAMF) test statistic. We then train a regression convolutional
neural network (CNN) to estimate target locations from these heatmap tensors,
and we compare the target localization accuracy of this approach with that of
peak-finding and local search methods. This empirical study shows that our
regression CNN achieves a considerable improvement in target location
estimation accuracy. The regression CNN offers significant gains and reasonable
accuracy even at signal-to-clutter-plus-noise ratio (SCNR) regimes that are
close to the breakdown threshold SCNR of the NAMF. We also study the robustness
of our trained CNN to mismatches in the radar data, where the CNN is tested on
heatmap tensors collected from areas that it was not trained on. We show that
our CNN can be made robust to mismatches in the radar data through few-shot
learning, using a relatively small number of new training samples.
