Graph Attention Network Based Single-Pixel Compressive Direction of Arrival Estimation
This provides a simplified, efficient DoA estimation method for radar and sensing applications, though it appears incremental as it applies GAT to an existing compressive sensing approach.
The paper tackles direction of arrival (DoA) estimation by using a graph attention network (GAT) with a single-pixel compressive radar framework, eliminating the need for reconstruction steps and achieving high-fidelity DoA retrieval even at low SNR levels.
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.