  In this paper, we present a single-pixel compressive direction of arrival
(DoA) estimation technique leveraging a graph attention network (GAT)-based
deep-learning framework. The physical layer compression is achieved using a
coded-aperture technique, probing the spectrum of far-field sources that are
incident on the aperture using a set of spatio-temporally incoherent modes.
This information is then encoded and compressed into the channel of the
coded-aperture. The coded-aperture is based on a metasurface antenna design and
it works as a receiver, exhibiting a single-channel and replacing the
conventional multichannel raster scan-based solutions for DoA estimation. The
GAT network enables the compressive DoA estimation framework to learn the DoA
information directly from the measurements acquired using the coded-aperture.
This step eliminates the need for an additional reconstruction step and
significantly simplifies the processing layer to achieve DoA estimation. We
show that the presented GAT integrated single-pixel radar framework can
retrieve high fidelity DoA information even under relatively low
signal-to-noise ratio (SNR) levels.
