Graph Attention Network Based Single-Pixel Compressive Direction of Arrival Estimation

TL;DR

This paper introduces a novel single-pixel compressive DoA estimation method using a graph attention network that simplifies processing and maintains high accuracy even at low SNR levels.

Contribution

It presents a GAT-based deep learning framework integrated with a metasurface antenna for direct compressive DoA estimation from single-pixel measurements, eliminating the need for reconstruction.

Findings

High fidelity DoA estimation at low SNR levels

Elimination of the reconstruction step simplifies processing

Effective use of a metasurface antenna for single-channel measurement

Abstract

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.