Target Detection and Positioning Aided by Reconfigurable Surfaces: Reflective or Holographic?

Reconfigurable metasurfaces integrating numerous elements are one promising solution for empowering high-accuracy positioning applications, benefiting from their high spatial resolution, low power consumption, and low cost. In this paper, we investigate two typical types of metasurfaces, i.e., reconfigurable holographic surfaces (RHSs) and reconfigurable intelligent surfaces (RISs), for target detection and positioning. Specifically, an RHS is a leaky-wave surface antenna with an embedded feed, while an RIS is a type of reflective metasurface whose feed is positioned outside the surface. Due to their distinct structures and working principles, RHSs and RISs may be suitable for different scenarios for target detection and positioning. To determine their best working scenarios, we first design the beamformers of both RIS-enabled and RHS-enabled radar systems to improve their performance. We then characterize the target detection and positioning performance analytically, and finally compare their performance in different scenarios. Theoretical and numerical results both reveal that: 1) in the one-dimensional linear array case, in general the performance of the RHS-enabled system is better than that of the RIS-enabled system; 2) in the two-dimensional planar array case, lower frequencies and larger physical sizes can contribute to a better performance of RIS-enabled systems than RHS-enabled systems, and vice versa.

I. Introduction

In response to the rapid growth of location-based applications like autonomous driving and the Internet of Things (IoT), the future 6G wireless systems are expected to possess advanced sensing functionalities characterized by high accuracy, low power consumption, and cost-effectiveness [1], [2], [3], [4], [5]. To realize this vision, massive multiple-input multiple-output (MIMO) technology based on large-scale phased arrays, which was originally proposed for high-performance communication, can be leveraged to achieve high-accuracy sensing performance as well. However, the intrinsic attributes of traditional phased arrays hinder their expansion to large sizes for future high-demanding sensing applications. Specifically, the reliance on power-consuming and expensive hardware components like phase shifters can result in an unaffordable increase in manufacturing costs and power consumption as the physical size of the phased array scales up.