  Using an amalgamation of techniques from classical radar, computer vision,
and deep learning, we characterize our ongoing data-driven approach to
space-time adaptive processing (STAP) radar. We generate a rich example dataset
of received radar signals by randomly placing targets of variable strengths in
a predetermined region using RFView, a site-specific radio frequency modeling
and simulation tool developed by ISL Inc. For each data sample within this
region, we generate heatmap tensors in range, azimuth, and elevation of the
output power of a minimum variance distortionless response (MVDR) beamformer,
which can be replaced with a desired test statistic. These heatmap tensors can
be thought of as stacked images, and in an airborne scenario, the moving radar
creates a sequence of these time-indexed image stacks, resembling a video. Our
goal is to use these images and videos to detect targets and estimate their
locations, a procedure reminiscent of computer vision algorithms for object
detection$-$namely, the Faster Region-Based Convolutional Neural Network
(Faster R-CNN). The Faster R-CNN consists of a proposal generating network for
determining regions of interest (ROI), a regression network for positioning
anchor boxes around targets, and an object classification algorithm; it is
developed and optimized for natural images. Our ongoing research will develop
analogous tools for heatmap images of radar data. In this regard, we will
generate a large, representative adaptive radar signal processing database for
training and testing, analogous in spirit to the COCO dataset for natural
images. As a preliminary example, we present a regression network in this paper
for estimating target locations to demonstrate the feasibility of and
significant improvements provided by our data-driven approach.
