A RIS-Enabled Computational Radar Coincidence Imaging

TL;DR

This paper presents a novel RIS-enabled radar coincidence imaging technique that combines reconfigurable surfaces with computational methods to achieve high-quality imaging with fewer measurements, improving SNR and reducing clutter.

Contribution

It introduces a new RIS-based framework for radar coincidence imaging that enhances image quality and efficiency compared to traditional raster scanning methods.

Findings

Achieves high-quality images with fewer measurements.

Provides higher SNR and reduced clutter.

Demonstrates effectiveness through numerical simulations.

Abstract

This paper introduces an innovative imaging method using reconfigurable intelligent surfaces (RISs) by combining radar coincidence imaging (RCI) and computational imaging techniques. In the proposed framework, RISs simultaneously redirect beams toward a desired region of interest (ROI). The interference of these beams forms spatially diverse speckle patterns that carry information about the entire ROI. As a result, this method can take advantage of the benefits of both random patterns and spotlight imaging. Since the speckle pattern is formed by directive beams (instead of random patterns typically used in computational imaging), this approach results in a higher signal-to-noise ratio (SNR) and reduced clutter. In contrast to raster scanning, which requires the number of measurements to be at least equal to the number of unknowns, our proposed approach follows a computational imaging framework and can obtain high-quality images even when only a few measurements are taken. Using numerical simulation, we demonstrate this method's capabilities and contrast it against other conventional techniques. The proposed imaging approach can be applied to security screening, wireless user tracking, and activity recognition.