Single-Antenna Non-Line-of-Sight Matrix Imaging via Reconfigurable Intelligent Surfaces

Modern imaging and sensing in complex environments, ranging from biomedical diagnostics to wireless communication, relies on accurately measuring and then controlling the wave propagation. Conventional approaches demand large arrays of antennas or transducers to reconstruct the full reflection or transmission matrix, enabling advanced protocols such as selective focusing or adaptive wave control. Yet, these arrays are expensive, bulky, and difficult to implement at microwave frequencies. Here, we show that a single transmitting-receiving antenna, when combined with a reconfigurable intelligent surface (RIS), can fully reconstruct the reflection matrix from far-field measurements, effectively transforming the RIS into a programmable synthetic antenna array. This approach allows high-fidelity imaging of complex scenes, selective focusing through clutter, and real-time tracking of moving targets. Our results establish RIS as a versatile, low-cost platform for matrix-based imaging, with broad implications for adaptive wave control, real-time sensing, and imaging in environments previously considered inaccessible.

💡 Research Summary

This paper presents a groundbreaking imaging methodology that circumvents the need for expensive and bulky multi-antenna arrays by leveraging a single transceiver antenna paired with a Reconfigurable Intelligent Surface (RIS). The core innovation lies in using the RIS as a programmable synthetic aperture, enabling the full reconstruction of the reflection matrix of a scene—a feat traditionally requiring complex Multiple-Input Multiple-Output (MIMO) hardware.

The authors establish a theoretical model where the RIS is represented as a system of coupled dipoles. This model captures the mutual interactions between RIS elements and their collective response to illumination from the single antenna. The process involves two critical phases. First, a calibration step is performed without the target scene. By measuring the antenna’s reflection coefficient for a large set of random RIS configurations and fitting these measurements to the coupled-dipole model via a gradient-descent optimization, all unknown system parameters are retrieved. These include the Green’s functions linking the antenna to each RIS element, the inter-element coupling matrix G_dd, and the polarizabilities of the RIS elements in their two states. Remarkably, this calibration allows the model to predict the system’s response to new, unseen RIS configurations with high accuracy (0.2% mean error).

Once calibrated, the target scene is introduced. The differential signal—the difference between measurements with and without the scene—is linearly related to the scene’s reflection matrix R, expressed in the basis of the RIS elements. By measuring this differential signal for a sequence of random RIS masks, the full reflection matrix R can be reconstructed analytically by solving a linear inverse problem. With R in hand, one can apply standard matrix-based imaging techniques, such as assuming free-space propagation to back-propagate the fields and reconstruct a spatial image of the scene’s reflectivity.

Experimental validation at 4.1 GHz using a 1-bit RIS with 128 elements demonstrated the technique’s efficacy. The calibration revealed Green’s functions closely matching free-space propagation and a near-π phase shift between element states. The reconstructed reflection matrix enabled high-fidelity imaging. The method requires a number of measurements on the order of the number of RIS elements, and when combined with the fast switching speed of RIS technology, it opens a path toward real-time, matrix-based imaging with a single antenna. This work generalizes beyond microwaves to optics and acoustics, positioning RIS as a versatile, low-cost platform for advanced wave control and imaging in previously inaccessible environments, with significant implications for sensing, communications, and diagnostics.