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.

翻译：在本文中,我们提出了一个单像压缩到货方向(DoA)估算技术,利用基于图形关注网络(GAT)的深学习框架。物理层压缩是使用一种编码孔径外光学技术实现的,用一套电磁模调不相容的模型来探测孔径上发生的远野源的频谱。此信息随后被编码并压缩到编码孔径的通道中。该编码孔径光基于一个元表层天线设计,它作为接收器发挥作用,展示一个单一通道,并取代传统的多通道光栅扫描法,用于 DoA 估计。GAT 网络使压缩DoA 估算框架能够直接从使用编码孔径的测量中获得的 DoA 信息。这一步骤消除了额外重建步骤的必要性,并大大简化了处理层,以实现 DoA 估计。我们表明,在相对低的信号到 NIS 水平下,GAT 集成的单像雷达框架可以检索高可靠性的 DoA 。