RIS-Aided Monostatic Sensing and Object Detection with Single and Double Bounce Multipath

TL;DR

This paper introduces a RIS-assisted monostatic sensing framework that leverages single- and double-bounce multipath signals for passive object detection, enhancing sensing capabilities in complex environments.

Contribution

The paper presents a novel RIS-aided sensing framework with adaptive detection and object estimation methods for single- and double-bounce multipath environments.

Findings

Effective passive object detection demonstrated through numerical simulations.

Enhanced sensing performance with RIS-assisted multipath exploitation.

Adaptive detection probabilities derived for geometric channel parameters.

Abstract

We propose a framework for monostatic sensing by a user equipment (UE), aided by a reconfigurable intelligent surface (RIS) in environments with single- and double-bounce signal propagation. We design appropriate UE-side precoding and combining, to facilitate signal separation. We derive the adaptive detection probabilities of the resolvable signals, based on the geometric channel parameters of the links. Then, we estimate the passive objects using both the double-bounce signals via passive RIS (i.e., RIS-sensing) and the single-bounce multipath direct to the objects (i.e., non-RIS-sensing), based on a mapping filter. Finally, we provide numerical results to demonstrate that effective sensing can be achieved through the proposed framework.